Kyriacou, S.K.,
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DOI Link
1003
BibRef
Earlier:
An Efficient Framework for Brain Tumor Segmentation in Magnetic
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IPTA08(1-5).
IEEE DOI
0811
BibRef
Bourouis, S.[Sami],
Hamrouni, K.[Kamel],
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Automatic MRI Brain Segmentation with Combined Atlas-Based
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Elsevier DOI
1102
BibRef
Earlier:
Multi-kernel SVM based classification for brain tumor segmentation of
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ICIP09(3373-3376).
IEEE DOI
0911
SVM; Segmentation; Feature selection; Fusion; Follow-up system; Brain
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BibRef
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Brain tumor segmentation from multiple MRI sequences using multiple
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ICIP14(1887-1891)
IEEE DOI
1502
Feature extraction
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DOI Link
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support vector machine, radial basis function, binarized image
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Tumor segmentation in brain MRI using a fuzzy approach with class
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JIVP(2014), No. 1, 2014, pp. 21.
DOI Link
1404
BibRef
Earlier:
Incorporating prior information in the fuzzy C-mean algorithm with
application to brain tissues segmentation in MRI,
ICIP09(3393-3396).
IEEE DOI
0911
BibRef
Chih-Feng, C.[Chen],
Ling-Wei, H.[Hsu],
Chun-Chung, L.[Lui],
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Hsu-Huei, W.[Weng],
Yuan-Hsiung, T.[Tsai],
Ho-Ling, L.[Liu],
In vivo correlation between semi-quantitative hemodynamic parameters
and Ktrans derived from DCE-MRI of brain tumors,
IJIST(22), No. 2, June 2012, pp. 132-136.
DOI Link
1202
BibRef
Beno, M.M.[M. Marsaline],
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bilateral filter
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Kwon, D.J.[Dong-Jin],
Niethammer, M.,
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Davatzikos, C.,
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MedImg(33), No. 3, March 2014, pp. 651-667.
IEEE DOI
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biomedical MRI
BibRef
Balasubramani, P.[Perumal],
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Efficient image compression techniques for compressing multimodal
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DOI Link
1506
MRI images
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Thapaliya, K.[Kiran],
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Identification and extraction of brain tumor from MRI using local
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IJIST(24), No. 4, 2014, pp. 284-292.
DOI Link
1411
Zernike polynomial
BibRef
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1502
brain tumor, clustering, segmentation, thresholding, Fuzzy c-means
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Arakeri, M.P.[Megha P.],
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Gamma Knife treatment planning: MR brain tumor segmentation and
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1509
semi-automatic segmentation
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d'Urso, D.[Davide],
Sabini, M.G.[Maria Gabriella],
Gambino, O.[Orazio],
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Springer DOI
1511
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biomedical MRI
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Biomedical image processing
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IEEE DOI
1109
BibRef
Pereira, S.,
Pinto, A.,
Alves, V.,
Silva, C.A.,
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI
Images,
MedImg(35), No. 5, May 2016, pp. 1240-1251.
IEEE DOI
1605
Brain modeling
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Thiruvenkadam, K.[Kalaiselvi],
Perumal, N.[Nagaraja],
Fully automatic method for segmentation of brain tumor from
multimodal magnetic resonance images using wavelet transformation and
clustering technique,
IJIST(26), No. 4, 2016, pp. 305-314.
DOI Link
1701
clustering, fuzzy c-means, segmentation, tumor, wavelet
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Farhi, L.[Lubna],
Yusuf, A.[Adeel],
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Active, contours
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Kaur, T.[Taranjit],
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IET-IPR(11), No. 8, August 2017, pp. 620-632.
DOI Link
1708
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Rajinikanth, V.,
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Fernandes, S.L.[Steven Lawrence],
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Entropy based segmentation of tumor from brain MR images:
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1708
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Alagarsamy, S.[Saravanan],
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A fully automated hybrid methodology using Cuckoo-based fuzzy
clustering technique for magnetic resonance brain image segmentation,
IJIST(27), No. 4, 2017, pp. 317-332.
DOI Link
1712
Cuckoo-Based Search, interval type-2 fuzzy clustering,
MR brain image segmentation, tumor identification
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Arnaud, A.,
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Coquery, N.,
Collomb, N.,
Lemasson, B.,
Barbier, E.L.,
Fully Automatic Lesion Localization and Characterization: Application
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MedImg(37), No. 7, July 2018, pp. 1678-1689.
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1808
biomedical MRI, brain, cancer, feature extraction,
Gaussian distribution, image segmentation, fingerprint model
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Pinto, A.[Adriano],
Pereira, S.[Sérgio],
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Elsevier DOI
1806
Brain tumour, Magnetic resonance imaging, Image segmentation,
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Ma, C.,
Luo, G.,
Wang, K.,
Concatenated and Connected Random Forests With Multiscale Patch
Driven Active Contour Model for Automated Brain Tumor Segmentation of
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MedImg(37), No. 8, August 2018, pp. 1943-1954.
IEEE DOI
1808
Image segmentation, Tumors, Brain modeling,
Magnetic resonance imaging, Active contours, Radio frequency,
multiscale patch
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Tang, Z.,
Ahmad, S.,
Yap, P.,
Shen, D.,
Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank
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MedImg(37), No. 10, October 2018, pp. 2224-2235.
IEEE DOI
1810
Brain, Tumors, Image segmentation, Pathology,
Convergence, Radiology, Low-rank,
multi-atlas segmentation
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Kermi, A.[Adel],
Andjouh, K.[Khaled],
Zidane, F.[Ferhat],
Fully automated brain tumour segmentation system in 3D-MRI using
symmetry analysis of brain and level sets,
IET-IPR(12), No. 11, November 2018, pp. 1964-1971.
DOI Link
1810
BibRef
Angulakshmi, M.,
Lakshmi Priya, G.G.,
Walsh Hadamard kernel-based texture feature for multimodal MRI brain
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IJIST(28), No. 4, December 2018, pp. 254-266.
WWW Link.
1811
BibRef
Zhou, T.,
Canu, S.,
Vera, P.,
Ruan, S.,
Latent Correlation Representation Learning for Brain Tumor
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IP(30), 2021, pp. 4263-4274.
IEEE DOI
2104
Tumors, Image segmentation, Correlation, Magnetic resonance imaging,
Brain modeling, Feature extraction, deep learning
BibRef
Chang, J.[Jie],
Zhang, L.[Luming],
Gu, N.J.[Nai-Jie],
Zhang, X.C.[Xiao-Ci],
Ye, M.Q.[Min-Quan],
Yin, R.Z.[Rong-Zhang],
Meng, Q.Q.[Qian-Qian],
A mix-pooling CNN architecture with FCRF for brain tumor segmentation,
JVCIR(58), 2019, pp. 316-322.
Elsevier DOI
1901
MR image segmentation, Convolutional Neural Network, Fully CRF
BibRef
Saman, S.[Sangeetha],
Narayanan, S.J.[Swathi Jamjala],
Survey on brain tumor segmentation and feature extraction of MR images,
MultInfoRetr(8), No. 2, June 2019, pp. 79-99.
Springer DOI
1906
Survey, Brain Tumors.
BibRef
Gtifa, W.[Wafa],
Hamdaoui, F.[Fayçal],
Sakly, A.[Anis],
3D brain tumor segmentation in MRI images based on a modified PSO
technique,
IJIST(29), No. 4, 2019, pp. 501-509.
DOI Link
1911
2D images, 3D brain tumor segmentation, modified particle swarm optimization
BibRef
Gilanie, G.[Ghulam],
Bajwa, U.I.[Usama Ijaz],
Waraich, M.M.[Mustansar Mahmood],
Habib, Z.[Zulfiqar],
Automated and reliable brain radiology with texture analysis of
magnetic resonance imaging and cross datasets validation,
IJIST(29), No. 4, 2019, pp. 531-538.
DOI Link
1911
brain tumor diagnosis, cross dataset validation,
MRI texture analysis, neoplastic and non-neoplastic tissues,
primary and secondary brain tumor
BibRef
Zhou, C.,
Ding, C.,
Wang, X.,
Lu, Z.,
Tao, D.,
One-Pass Multi-Task Networks With Cross-Task Guided Attention for
Brain Tumor Segmentation,
IP(29), 2020, pp. 4516-4529.
IEEE DOI
2003
Tumors, Task analysis, Image segmentation, Brain modeling,
Computational modeling, Training, Magnetic resonance imaging,
channel attention
BibRef
Dandil, E.[Emre],
Biçer, A.[Ali],
Automatic grading of brain tumours using LSTM neural networks on
magnetic resonance spectroscopy signals,
IET-IPR(14), No. 10, August 2020, pp. 1967-1979.
DOI Link
2008
BibRef
Afshar, P.,
Mohammadi, A.,
Plataniotis, K.N.,
BayesCap: A Bayesian Approach to Brain Tumor Classification Using
Capsule Networks,
SPLetters(27), 2020, pp. 2024-2028.
IEEE DOI
2012
BibRef
Earlier: A1, A3, A2:
Capsule Networks' Interpretability for Brain Tumor Classification Via
Radiomics Analyses,
ICIP19(3816-3820)
IEEE DOI
1910
BibRef
And: A1, A2, A3:
Brain Tumor Type Classification via Capsule Networks,
ICIP18(3129-3133)
IEEE DOI
1809
Bayes methods, Uncertainty, Tumors, Predictive models, Deep learning,
Measurement uncertainty, Brain modeling,
Radiomics.
Brain Tumor Classification, Capsule Networks, Explainability.
Feature extraction, Magnetic resonance imaging, Neurons,
Cancer, Training data,
Convolutional neural networks
BibRef
Afshar, P.,
Shahroudnejad, A.,
Mohammadi, A.,
Plataniotis, K.N.,
CARISI: Convolutional Autoencoder-Based Inter-Slice Interpolation of
Brain Tumor Volumetric Images,
ICIP18(1458-1462)
IEEE DOI
1809
Tumors, Interpolation,
Image reconstruction, Shape, Computed tomography, Convolution,
Convolutional auto-encoder
BibRef
Amin, J.[Javaria],
Sharif, M.[Muhammad],
Gul, N.[Nadia],
Yasmin, M.[Mussarat],
Shad, S.A.[Shafqat Ali],
Brain tumor classification based on DWT fusion of MRI sequences using
convolutional neural network,
PRL(129), 2020, pp. 115-122.
Elsevier DOI
2001
Sequences, CNN, DWT, Global thresholding, Filter
BibRef
Sharif, M.I.[Muhammad Irfan],
Li, J.P.[Jian Ping],
Khan, M.A.[Muhammad Attique],
Saleem, M.A.[Muhammad Asim],
Active deep neural network features selection for segmentation and
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PRL(129), 2020, pp. 181-189.
Elsevier DOI
2001
Brain tumor, Contrast improvement, Deep saliency method,
Features extraction, Optimization, Recognition
BibRef
Mammoli, D.,
Gordon, J.,
Autry, A.,
Larson, P.E.Z.,
Li, Y.,
Chen, H.,
Chung, B.,
Shin, P.,
van Criekinge, M.,
Carvajal, L.,
Slater, J.B.,
Bok, R.,
Crane, J.,
Xu, D.,
Chang, S.,
Vigneron, D.B.,
Kinetic Modeling of Hyperpolarized Carbon-13 Pyruvate Metabolism in
the Human Brain,
MedImg(39), No. 2, February 2020, pp. 320-327.
IEEE DOI
2002
Brain cancer, dissolution dynamic nuclear polarization,
hyperpolarized MRI, kinetic modeling, kPL, kPB, metabolic imaging
BibRef
Hachemi, B.[Belkacem],
Chama, Z.[Zouaoui],
Alim-Ferhat, F.[Fatiha],
Lamini, E.S.[El-Sedik],
Abderrahmane, A.[Abdelkader],
Anani, M.[Macho],
Choquet, C.[Catherine],
Fully automatic multisegmentation approach for magnetic resonance
imaging brain tumor detection using improved region-growing and
quasi-Monte Carlo-expectation maximization algorithm,
IJIST(30), No. 1, 2020, pp. 104-111.
DOI Link
2002
brain tumor, expectation maximization, multisegmentation,
quasi-Monte Carlo, region growing
BibRef
Chithra, P.L.,
Dheepa, G.,
Di-phase midway convolution and deconvolution network for brain tumor
segmentation in MRI images,
IJIST(30), No. 3, 2020, pp. 674-686.
DOI Link
2008
brain tumor segmentation,
di-phase midway convolution and deconvolution network,
upsampling
BibRef
Zhang, D.,
Huang, G.,
Zhang, Q.,
Han, J.,
Han, J.,
Wang, Y.,
Yu, Y.,
Exploring Task Structure for Brain Tumor Segmentation From
Multi-Modality MR Images,
IP(29), 2020, pp. 9032-9043.
IEEE DOI
2009
Tumors, Task analysis, Image segmentation,
Brain modeling, supervised learning
BibRef
Kurmi, Y.[Yashwant],
Chaurasia, V.[Vijayshri],
Classification of magnetic resonance images for brain tumour detection,
IET-IPR(14), No. 12, October 2020, pp. 2808-2818.
DOI Link
2010
BibRef
Mano, A.[Abhisha],
Anand, S.[Swaminathan],
Method of multi-region tumour segmentation in brain MRI images using
grid-based segmentation and weighted bee swarm optimisation,
IET-IPR(14), No. 12, October 2020, pp. 2901-2910.
DOI Link
2010
BibRef
Amin, J.[Javeria],
Sharif, M.[Muhammad],
Yasmin, M.[Mussarat],
Fernandes, S.L.[Steven Lawrence],
A distinctive approach in brain tumor detection and classification
using MRI,
PRL(139), 2020, pp. 118-127.
Elsevier DOI
2011
Cells, Tumors, Segmentation, Lesion, Tissues
BibRef
Yepuganti, K.[Karuna],
Saladi, S.[Saritha],
Narasimhulu, C.V.,
Segmentation of tumor using PCA based modified fuzzy C means
algorithms on MR brain images,
IJIST(30), No. 4, 2020, pp. 1337-1345.
DOI Link
2011
brain tumor, DWT, feature extraction, fuzzy C means and MRI
BibRef
Ragupathy, B.[Balakumaresan],
Karunakaran, M.[Manivannan],
A fuzzy logic-based meningioma tumor detection in magnetic resonance
brain images using CANFIS and U-Net CNN classification,
IJIST(31), No. 1, 2021, pp. 379-390.
DOI Link
2102
brain, classifications, features, meningioma, tumor
BibRef
Kakarla, J.[Jagadeesh],
Isunuri, B.V.[Bala Venkateswarlu],
Doppalapudi, K.S.[Krishna Sai],
Bylapudi, K.S.R.[Karthik Satya Raghuram],
Three-class classification of brain magnetic resonance images using
average-pooling convolutional neural network,
IJIST(31), No. 3, 2021, pp. 1731-1740.
DOI Link
2108
average pooling, brain tumor classification,
brain tumor dataset, convolutional neural network, three-class classification
BibRef
Anantharajan, S.[Shenbagarajan],
Gunasekaran, S.[Shenbagalakshmi],
Automated brain tumor detection and classification using weighted
fuzzy clustering algorithm, deep auto encoder with barnacle mating
algorithm and random forest classifier techniques,
IJIST(31), No. 4, 2021, pp. 1970-1988.
DOI Link
2112
barnacle mating algorithm, deep auto-encoder,
magnetic resonance imaging, random forest classifier,
weighted fuzzy clustering algorithm
BibRef
Zhang, W.B.[Wen-Bo],
Yang, G.[Guang],
Huang, H.[He],
Yang, W.J.[Wei-Ji],
Xu, X.M.[Xiao-Mei],
Liu, Y.K.[Yong-Kai],
Lai, X.B.[Xiao-Bo],
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation,
IJIST(31), No. 4, 2021, pp. 1834-1848.
DOI Link
2112
automatic segmentation, brain tumor segmentation,
deep learning, magnetic resonance imaging, multi-encoder net
BibRef
Sran, P.K.[Paramveer Kaur],
Gupta, S.[Savita],
Singh, S.[Sukhwinder],
Integrating saliency with fuzzy thresholding for brain tumor
extraction in MR images,
JVCIR(74), 2021, pp. 102964.
Elsevier DOI
2101
Saliency, Fuzzy, Segmentation, ROI, Medical Images
BibRef
Ragupathy, B.[Balakumaresan],
Karunakaran, M.[Manivannan],
A deep learning model integrating convolution neural network and
multiple kernel K means clustering for segmenting brain tumor in
magnetic resonance images,
IJIST(31), No. 1, 2021, pp. 118-127.
DOI Link
2102
convolutional neural network, magnetic resonance image,
multi kernel K means clustering, segmentation, tumor detection
BibRef
Thiruvenkadam, K.[Kalaiselvi],
Nagarajan, K.[Kalaichelvi],
Fully automatic brain tumor extraction and tissue segmentation from
multimodal MRI brain images,
IJIST(31), No. 1, 2021, pp. 336-350.
DOI Link
2102
3D volume estimation, bit plane slicing, brain tumor,
extended maxima, FCM, inverse log transform, MRI
BibRef
Shirali, A.[Armaghan],
Shiri, N.[Nabiollah],
Diagnosis of brain tumours by MRI binarisation with variable fuzzy
level,
IET-IPR(14), No. 16, 19 December 2020, pp. 4269-4276.
DOI Link
2103
BibRef
Kaushal, B.[Bhakti],
Patil, M.D.[Mukesh D.],
Birajdar, G.K.[Gajanan K.],
Fractional wavelet transform based diagnostic system for brain tumor
detection in MR imaging,
IJIST(31), No. 2, 2021, pp. 575-591.
DOI Link
2105
brain tumor detection, discrete wavelet transform,
fractional Fourier transform, fractional wavelet transform
BibRef
Arumugam, S.[Selvapandian],
Paulraj, S.[Sivakumar],
Selvaraj, N.P.[Nagendra Prabhu],
Brain MR image tumor detection and classification using neuro fuzzy
with binary cuckoo search technique,
IJIST(31), No. 3, 2021, pp. 1185-1196.
DOI Link
2108
binary cuckoo search, magnetic resonance imaging,
neural network, neuro fuzzy with binary cuckoo search,
singular value decomposition
BibRef
Tunga, P.P.[P. Prakash],
Singh, V.[Vipula],
Aditya, V.S.[V. Sri],
Subramanya, N.,
U-Net Model-Based Classification and Description of Brain Tumor in MRI
Images,
IJIG(21), No. 5 2021, pp. 2140005.
DOI Link
2201
BibRef
Fang, L.L.[Ling-Ling],
Wang, X.[Xin],
Brain tumor segmentation based on the dual-path network of
multi-modal MRI images,
PR(124), 2022, pp. 108434.
Elsevier DOI
2203
Brain tumor segmentation, Deep learning, Dual-path model,
Magnetic resonance imaging, Multi-modal images
BibRef
Shanmugam, S.[Sasikanth],
Surampudi, S.R.[Srinivasa Rao],
A method for detecting and classifying the tumor regions in brain MRI
images using vector index filtering and ANFIS classification process,
IJIST(32), No. 2, 2022, pp. 687-696.
DOI Link
2203
ANFIS, BRATS, fast wavelet transform,
magnetic resonance imaging, vector index filtering
BibRef
Asthana, P.[Pallavi],
Hanmandlu, M.[Madasu],
Vashisth, S.[Sharda],
Classification of brain tumor from magnetic resonance images using
probabilistic features and possibilistic Hanman-Shannon transform
classifier,
IJIST(32), No. 1, 2022, pp. 280-294.
DOI Link
2201
brain tumor, classification, feature extraction,
Hanman-Shannon transform classifier,
probabilistic features
BibRef
Sun, M.L.[Meng-Li],
Zou, W.[Wei],
Hu, N.[Nan],
Wang, J.J.[Jia-Jun],
Chi, Z.[Zheru],
Iterative brain tumor retrieval for MR images based on user's
intention model,
PR(127), 2022, pp. 108650.
Elsevier DOI
2205
CBIR, Brain tumor images, Eye-tracking, Intention similarity,
Iterative retrieval, Relevance feedback
BibRef
Alpar, O.[Orcan],
Dolezal, R.[Rafael],
Ryska, P.[Pavel],
Krejcar, O.[Ondrej],
Nakagami-Fuzzy imaging framework for precise lesion segmentation in
MRI,
PR(128), 2022, pp. 108675.
Elsevier DOI
2205
Nakagami imaging, Fuzzy c-means, Lesion segmentation, MRI, BraTS
BibRef
Liu, F.[Fan],
Li, D.X.[Dong-Xiao],
Jin, X.Y.[Xin-Yu],
Qiu, W.Y.[Wen-Yuan],
Accelerated brain tumor dynamic contrast-enhanced MRI using Adaptive
Pharmaco-Kinetic Model Constrained method,
IJIST(32), No. 3, 2022, pp. 728-739.
DOI Link
2205
accelerated DCE-MRI, adaptive dictionary, brain tumor,
compressed sensing, Pharmacokinetic modeling
BibRef
Shivaprasad, B.J.,
Ravikumar, M.,
Guru, D.S.,
Analysis of Brain Tumor Using MR Images: A Brief Survey,
IJIG(22), No. 2, April 2022, pp. 2250023.
DOI Link
2205
BibRef
Liu, Y.[Yu],
Mu, F.[Fuhao],
Shi, Y.[Yu],
Chen, X.[Xun],
SF-Net: A Multi-Task Model for Brain Tumor Segmentation in Multimodal
MRI via Image Fusion,
SPLetters(29), 2022, pp. 1799-1803.
IEEE DOI
2209
Image segmentation, Tumors, Task analysis, Multitasking,
Brain modeling, Image fusion, Training, Brain tumor segmentation,
multi-task learning
BibRef
Khan, M.A.[Muhammad Attique],
Khan, A.[Awais],
Alhaisoni, M.[Majed],
Alqahtani, A.[Abdullah],
Alsubai, S.[Shtwai],
Alharbi, M.[Meshal],
Malik, N.A.[Nazir Ahmed],
Damaševicius, R.[Robertas],
Multimodal brain tumor detection and classification using deep
saliency map and improved dragonfly optimization algorithm,
IJIST(33), No. 2, 2023, pp. 572-587.
DOI Link
2303
brain tumor, contrast enhancement, deep learning, fusion,
improved dragon fly optimization, MRI
BibRef
Ragupathy, B.[Balakumaresan],
Subramani, B.[Bharath],
Arumugam, S.[Selvapandian],
A novel approach for MR brain tumor classification and detection
using optimal CNN-SVM model,
IJIST(33), No. 2, 2023, pp. 746-759.
DOI Link
2303
anisotropic diffusion, brain tumor,
convolutional neural network, morphological operations, support vector machine
BibRef
Yang, Q.S.[Qiu-Shi],
Guo, X.Q.[Xiao-Qing],
Chen, Z.[Zhen],
Woo, P.Y.M.[Peter Y. M.],
Yuan, Y.X.[Yi-Xuan],
D2-Net: Dual Disentanglement Network for Brain Tumor Segmentation
With Missing Modalities,
MedImg(41), No. 10, October 2022, pp. 2953-2964.
IEEE DOI
2210
Tumors, Image segmentation, Magnetic resonance imaging,
Feature extraction, Correlation, Codes, Brain modeling, missing modalities
BibRef
Ghahramani, M.[Marzieh],
Shiri, N.[Nabiollah],
Brain tumour detection in magnetic resonance imaging using
Levenberg-Marquardt backpropagation neural network,
IET-IPR(17), No. 1, 2023, pp. 88-103.
DOI Link
2301
BibRef
Huang, T.Y.[Tong-Yuan],
Liu, Y.[Yao],
Research on the magnetic resonance imaging brain tumor segmentation
algorithm based on DO-UNet,
IJIST(33), No. 1, 2023, pp. 143-157.
DOI Link
2301
attention mechanism, brain tumor segmentation,
deep over-parameterized convolution, multi-scale features, U-net model
BibRef
Kollem, S.[Sreedhar],
Reddy, K.R.[Katta Ramalinga],
Prasad, C.R.[Ch. Rajendra],
Chakraborty, A.[Avishek],
Ajayan, J.,
Sreejith, S.,
Bhattacharya, S.[Sandip],
Leo Joseph, L.M.I.,
Janapati, R.[Ravichander],
AlexNet-NDTL: Classification of MRI brain tumor images using modified
AlexNet with deep transfer learning and Lipschitz-based data
augmentation,
IJIST(33), No. 4, 2023, pp. 1306-1322.
DOI Link
2307
AlexNet, data augmentation, deep neural networks,
magnetic resonance imaging, transfer learning
BibRef
Cinar, N.[Necip],
Kaya, M.[Mehmet],
Kaya, B.[Buket],
A novel convolutional neural network-based approach for brain tumor
classification using magnetic resonance images,
IJIST(33), No. 3, 2023, pp. 895-908.
DOI Link
2305
artificial neural network models, brain tumor classification,
convolutional neural network, deep learning
BibRef
Lin, J.W.[Jian-Wei],
Lin, J.[Jiatai],
Lu, C.[Cheng],
Chen, H.[Hao],
Lin, H.[Huan],
Zhao, B.C.[Bing-Chao],
Shi, Z.W.[Zhen-Wei],
Qiu, B.J.[Bing-Jiang],
Pan, X.P.[Xi-Peng],
Xu, Z.[Zeyan],
Huang, B.[Biao],
Liang, C.H.[Chang-Hong],
Han, G.Q.[Guo-Qiang],
Liu, Z.[Zaiyi],
Han, C.[Chu],
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With
Modality-Correlated Cross-Attention for Brain Tumor Segmentation,
MedImg(42), No. 8, August 2023, pp. 2451-2461.
IEEE DOI
2308
Transformers, Tumors, Brain modeling, Image segmentation,
Magnetic resonance imaging, Feature extraction, multi-modal fusion
BibRef
Gull, S.[Sahar],
Akbar, S.[Shahzad],
Naqi, S.M.[Syed Muhammad],
A deep learning approach for multi-stage classification of brain
tumor through magnetic resonance images,
IJIST(33), No. 5, 2023, pp. 1745-1766.
DOI Link
2310
brain tumor segmentation, convolutional neural network, deep learning,
fast bounding box, magnetic resonance imaging, multistage classification
BibRef
Shaker, E.A.[Esraa Asem],
El-Hossiny, A.S.[Ahmed S.],
Kandil, A.H.[Ahmed Hisham],
Elbialy, A.[Ahmed],
Afify, H.M.[Heba M.],
Advanced imaging system for brain tumor automatic classification from
MRI images using HOG and BOF feature extraction approaches,
IJIST(33), No. 5, 2023, pp. 1661-1671.
DOI Link
2310
brain tumor images, classification, feature extraction,
machine learning, magnetic resonance imaging
BibRef
Krishnasamy, N.[Narayanan],
Ponnusamy, T.[Thangaraj],
Deep learning-based robust hybrid approaches for brain tumor
classification in magnetic resonance images,
IJIST(33), No. 6, 2023, pp. 2157-2177.
DOI Link
2311
brain tumor classification, deep learning,
fully convolutional networks, magnetic resonance imaging,
residual networks threshold-based segmentation
BibRef
Khan, S.U.R.[Saif Ur Rehman],
Zhao, M.[Ming],
Asif, S.[Sohaib],
Chen, X.[Xuehan],
Hybrid-NET: A fusion of DenseNet169 and advanced machine learning
classifiers for enhanced brain tumor diagnosis,
IJIST(34), No. 1, 2024, pp. e22975.
DOI Link
2401
brain tumor, DenseNet169, machine learning classifier, MRI, transfer learning
BibRef
Agrawal, T.[Tarun],
Choudhary, P.[Prakash],
Shankar, A.[Achyut],
Singh, P.[Prabhishek],
Diwakar, M.[Manoj],
MultiFeNet: Multi-scale feature scaling in deep neural network for
the brain tumour classification in MRI images,
IJIST(34), No. 1, 2024, pp. e22956.
DOI Link
2401
brain tumour detection, deep learning, hybrid pooling,
MRI images, multi-scale feature
BibRef
Mehrotra, R.[Rajat],
Ansari, M.A.,
Agrawal, R.[Rajeev],
Al-Ward, H.[Hisham],
Tripathi, P.[Pragati],
Singh, J.[Jay],
An enhanced framework for identifying brain tumor using discrete
wavelet transform, deep convolutional network, and feature
fusion-based machine learning techniques,
IJIST(34), No. 1, 2024, pp. e22983.
DOI Link
2401
brain tumor, deep convolutional network, DWT, feature fusion, MRI
BibRef
Singh, N.H.[Ngangbam Herojit],
Merlin, N.R.G.[N. R. Gladiss],
Prabu, R.T.[R. Thandaiah],
Gupta, D.[Deepak],
Alharbi, M.[Meshal],
Multi-classification of brain tumor by using deep convolutional
neural network model in magnetic resonance imaging images,
IJIST(34), No. 1, 2024, pp. e22951.
DOI Link
2401
brain tumor, classification, convolutional neural network,
deep learning, HPSGWO
BibRef
Datta, P.[Priyanka],
Rohilla, R.[Rajesh],
Brain tumor image pixel segmentation and detection using an
aggregation of GAN models with vision transformer,
IJIST(34), No. 1, 2024, pp. e22979.
DOI Link
2401
brain cancer, generative adversarial networks,
magnetic resonance imaging, pixel segmentation,
vision transformer
BibRef
Ay, S.[Sevket],
Ekinci, E.[Ekin],
Garip, Z.[Zeynep],
A brain tumour classification on the magnetic resonance images using
convolutional neural network based privacy-preserving federated
learning,
IJIST(34), No. 1, 2024, pp. e23018.
DOI Link
2401
classification, deep learning, federated learning, privacy-preserving
BibRef
Wang, Z.K.[Ze-Kun],
Zou, Y.[Yanni],
Chen, H.Y.[Hong-Yu],
Liu, P.X.[Peter X.],
Chen, J.Y.[Jun-Yu],
Multi-scale features and attention guided for brain tumor
segmentation,
JVCIR(100), 2024, pp. 104141.
Elsevier DOI
2405
Brain tumor segmentation, Convolutional neural network,
Magnetic resonance images, Dilated convolution
BibRef
Hu, Y.J.[Yuan-Jing],
Huang, A.[Aibin],
Xu, R.[Rui],
HAB-Net: Hierarchical asymmetric convolution and boundary enhancement
network for brain tumor segmentation,
IET-IPR(18), No. 7, 2024, pp. 1809-1822.
DOI Link
2405
Brain tumor segmentation, Boundary attention,
hierarchical convolution, magnetic resonance images, Transformer
BibRef
Mazher, M.[Moona],
Qayyum, A.[Abdul],
Puig, D.[Domenec],
Abdel-Nasser, M.[Mohamed],
Deep learning-based survival prediction of brain tumor patients using
attention-guided 3D convolutional neural network with radiomics
approach from multimodality magnetic resonance imaging,
IJIST(34), No. 1, 2024, pp. e23010.
DOI Link
2401
brain tumor, brain tumor prognosis, deep learning,
medical image processing, multimodal brain tumor, radiomics,
survival prediction
BibRef
Haimour, F.[Fatima],
Al-Sayyed, R.[Rizik],
Mahafza, W.[Waleed],
Al-Kadi, O.S.[Omar S.],
Bidirectional brain image translation using transfer learning from
generic pre-trained models,
CVIU(248), 2024, pp. 104100.
Elsevier DOI
2409
Image translation, Transfer learning, Pre-trained model,
Brain tumor, Magnetic resonance imaging, Computed tomography, CycleGAN
BibRef
Ullah, N.[Naeem],
Hassan, M.[Muhammad],
Khan, J.A.[Javed Ali],
Anwar, M.S.[Muhammad Shahid],
Aurangzeb, K.[Khursheed],
Enhancing explainability in brain tumor detection: A novel
DeepEBTDNet model with LIME on MRI images,
IJIST(34), No. 1, 2024, pp. e23012.
DOI Link
2401
brain-tumor detection, deep learning, explainable AI, LIME, MRI
BibRef
Su, J.[Jianpo],
Shen, H.[Hui],
Peng, L.M.[Li-Min],
Hu, D.[Dewen],
Few-Shot Domain-Adaptive Anomaly Detection for Cross-Site Brain
Images,
PAMI(46), No. 3, March 2024, pp. 1819-1835.
IEEE DOI
2402
Anomaly detection, Mental disorders,
Functional magnetic resonance imaging, Training,
fMRI functional connectivity
BibRef
Bhuyan, R.[Ranadeep],
Nandi, G.[Gypsy],
Systematic study and design of multimodal MRI image augmentation for
brain tumor detection with loss aware exchange and residual networks,
IJIST(34), No. 2, 2024, pp. e22989.
DOI Link
2402
brain tumor, data augmentation, deep learning,
magnetic resonance imaging (MRI), residual learning, UNet
BibRef
Vatanpour, M.[Marjan],
Haddadnia, J.[Javad],
Brain tumour segmentation of MR images based on custom attention
mechanism with transfer-learning,
IET-IPR(18), No. 4, 2024, pp. 886-896.
DOI Link
2403
image segmentation, medical image processing
BibRef
Singha, A.[Anu],
Venkateswaran, V.[Vanitha],
A Study on Effective Segmentation Network in MRI Images for Diagnosis
of Brain Tumor,
ICCVMI23(1-7)
IEEE DOI
2403
Shape, Magnetic resonance imaging, Semantic segmentation,
Computational modeling, skip connections
BibRef
Ghahramani, M.[Marzieh],
Shiri, N.[Nabiollah],
An adaptive neuro-fuzzy inference system optimized by genetic
algorithm for brain tumour detection in magnetic resonance images,
IET-IPR(18), No. 5, 2024, pp. 1358-1372.
DOI Link
2404
biomedical MRI, neural net architecture, tumours
BibRef
Zhu, Z.Q.[Zhi-Qin],
Wang, Z.Y.[Zi-Yu],
Qi, G.Q.[Guan-Qiu],
Mazur, N.[Neal],
Yang, P.[Pan],
Liu, Y.[Yu],
Brain tumor segmentation in MRI with multi-modality spatial
information enhancement and boundary shape correction,
PR(153), 2024, pp. 110553.
Elsevier DOI
2405
Brain tumor segmentation, Multi-modality MRI,
Spatial information enhancement, Boundary shape correction
BibRef
Wen, J.Y.[Jin-Yu],
Khan, A.[Asad],
Chen, A.[Amei],
Peng, W.L.[Wei-Long],
Fang, M.[Meie],
Chen, C.L.P.[C. L. Philip],
Li, P.[Ping],
High-Quality Fusion and Visualization for MR-PET Brain Tumor Images
via Multi-Dimensional Features,
IP(33), 2024, pp. 3550-3563.
IEEE DOI Code:
WWW Link.
2406
Feature extraction, Image fusion, Wavelet transforms,
Image color analysis, Deep learning, Visualization, affine transformation
BibRef
An, D.L.[Dian-Long],
Liu, P.P.[Pan-Pan],
Feng, Y.[Yan],
Ding, P.J.[Peng-Ju],
Zhou, W.F.[Wei-Feng],
Yu, B.[Bin],
Dynamic weighted knowledge distillation for brain tumor segmentation,
PR(155), 2024, pp. 110731.
Elsevier DOI Code:
WWW Link.
2408
Brain tumor segmentation, MRI, Static knowledge distillation,
Dynamic weighted knowledge distillation, Interpretability
BibRef
Mundada, K.[Kapil],
Kulkarni, J.[Jayant],
MRI Image-Based Automatic Segmentation and Classification of Brain
Tumor and Swelling Using Novel Methodologies,
IJIG(24), No. 6, November 2024, pp. 2450051.
DOI Link
2501
BibRef
Shen, X.Y.[Xiao-Yan],
Wang, J.[Ju],
Zhao, Y.H.[Yu-Hua],
Zhou, R.[Rui],
Gao, H.[Han],
Zhang, J.K.[Jia-Kai],
Shen, H.M.[Hong-Ming],
MRS-Net: Brain tumour segmentation network based on feature fusion
and attention mechanism,
IET-IPR(18), No. 14, 2024, pp. 4542-4550.
DOI Link
2501
image processing, image segmentation
BibRef
Hu, Y.J.[Yuan-Jing],
Huang, A.[Aibin],
LFBTS: Enhanced Multimodality MRI Fusion for Brain Tumor Segmentation
With Limited Computational Resources,
IJIST(35), No. 2, 2025, pp. e70044.
DOI Link
2502
brain tumor segmentation, deep learning, image fusion,
lightweight, transformer
BibRef
Chandni,
Sachdeva, M.[Monika],
Kushwaha, A.K.S.[Alok Kumar Singh],
AI-based intelligent hybrid framework (BO-DenseXGB) for multi-
classification of brain tumor using MRI,
IVC(154), 2025, pp. 105417.
Elsevier DOI
2502
Computer aided diagnosis, Deep learning, Brain tumor,
Machine learning, Tumor classification
BibRef
Lv, C.J.[Chun-Jie],
Li, B.Y.[Bi-Yuan],
Wang, X.W.[Xiu-Wei],
Cai, P.F.[Peng-Fei],
Yang, B.[Bo],
Jia, X.F.[Xue-Feng],
Yan, J.[Jun],
CMS-net: Edge-aware multimodal MRI feature fusion for brain tumor
segmentation,
IVC(156), 2025, pp. 105481.
Elsevier DOI
2503
Feature fusion, Boundary awareness, Spatial state,
Cross-channel feature extraction, Dual-encoder, Brain tumor segmentation
BibRef
Jiang, B.[Bin],
Liao, M.Y.[Mao-Yu],
Zhao, Y.[Yun],
Li, G.[Gen],
Cheng, S.[Siyu],
Wang, X.K.[Xiang-Kai],
Xia, Q.L.[Qing-Ling],
Deep learning for brain tumor segmentation in multimodal MRI images:
A review of methods and advances,
IVC(156), 2025, pp. 105463.
Elsevier DOI
2503
Medical image segmentation, Brain tumor,
Magnetic resonance imaging, Multimodality
BibRef
Min, J.[Jie],
Huang, T.[Tongyuan],
Huang, B.[Boxiong],
Hu, C.X.[Chuan-Xin],
Zhang, Z.X.[Zhi-Xing],
KIDBA-Net: A Multi-Feature Fusion Brain Tumor Segmentation Network
Utilizing Kernel Inception Depthwise Convolution and Bi-Cross
Attention,
IJIST(35), No. 2, 2025, pp. e70055.
DOI Link
2504
Bi-Cross Attention, brain tumor segmentation,
encoder-decoder architecture, MRI
BibRef
Kunjumon, A.[Anila],
Jacob, C.[Chinnu],
Resmi, R.,
Three-Dimensional Network With Squeeze and Excitation for Accurate
Multi-Region Brain Tumor Segmentation,
IJIST(35), No. 2, 2025, pp. e70057.
DOI Link
2504
brain tumor, MRI, segmentation, squeeze and excitation (SE), U-net
BibRef
Nejad, M.M.[Mojtaba Mansouri],
Rostami, H.[Habib],
Keshavarz, A.[Ahmad],
Ghimatgar, H.[Hojat],
Rayani, M.S.[Mohamad Saleh],
Gonbadi, L.[Leila],
Leveraging Local and Global Features for Enhanced Segmentation of
Brain Metastatic Tumors in Magnetic Resonance Imaging,
IJIST(35), No. 2, 2025, pp. e70042.
DOI Link
2504
CNN, MRI, VIT
BibRef
Wu, B.[Bo],
Shi, D.H.[Dong-Hui],
Aguilar, J.[Jose],
Brain Tumors Classification in MRIs Based on Personalized Federated
Distillation Learning With Similarity-Preserving,
IJIST(35), No. 2, 2025, pp. e70046.
DOI Link
2504
brain tumor, knowledge distillation,
non-independent identically distributed data, similarity-preserving
BibRef
Ding, Y.H.[Yu-Hang],
Liu, H.M.[Hong-Min],
Barely-Supervised Brain Tumor Segmentation via Employing Segment
Anything Model,
CirSysVideo(35), No. 4, April 2025, pp. 2975-2986.
IEEE DOI
2504
Image segmentation, Tumors, Training, Biomedical imaging,
Knowledge engineering, Annotations, Brain modeling,
multi-modal MRI images
BibRef
Zhou, T.X.[Tong-Xue],
Boundary-aware and cross-modal fusion network for enhanced
multi-modal brain tumor segmentation,
PR(165), 2025, pp. 111637.
Elsevier DOI
2505
Brain tumor segmentation, Boundary detection,
Multi-modal fusion, Deep learning, MR modalities
BibRef
Qiu, Y.S.[Yan-Sheng],
Jiang, K.[Kui],
Yao, H.[Hongdou],
Wang, Z.[Zheng],
Satoh, S.[Shin'ichi],
Does Adding a Modality Really Make Positive Impacts in Incomplete
Multi-Modal Brain Tumor Segmentation?,
MedImg(44), No. 5, May 2025, pp. 2194-2205.
IEEE DOI
2505
Reliability, Brain tumors, Image segmentation, Uncertainty,
Magnetic resonance imaging, Training, Protocols, Predictive models,
positive/negative impacts regions
BibRef
Sadr, H.[Hossein],
Nazari, M.[Mojdeh],
Yousefzadeh-Chabok, S.[Shahrokh],
Emami, H.[Hassan],
Rabiei, R.[Reza],
Ashraf, A.[Ali],
Enhancing brain tumor classification in MRI images: A deep
learning-based approach for accurate diagnosis,
IVC(159), 2025, pp. 105555.
Elsevier DOI
2505
Brain tumor, Deep learning, Convolutional neural network,
Magnetic resonance imaging, Data augmentation
BibRef
Sa, B.K.[Bijay Kumar],
Agrawal, S.[Sanjay],
Panda, R.[Rutuparna],
A Leakage-Resistant Spatially Weighted Active Contour for Brain Tumor
Segmentation,
IJIST(35), No. 3, 2025, pp. e70110.
DOI Link
2506
active contour, biomedical images, brain MR image, cancer,
level-set, segmentation, tumor, ultrasound image
BibRef
Salve, A.K.[Amrapali Kishanrao],
Jondhale, K.C.[Kalpana C.],
An accurate and efficient multi-task brain tumour detection with
segmented MRI images using auto-metric adolescent neural network,
IJCVR(15), No. 4, 2025, pp. 470-487.
DOI Link
2507
BibRef
Huang, S.Q.[Shao-Qiong],
Huang, M.X.[Meng-Xing],
Zhang, Y.[Yu],
Chen, J.[Jing],
Bhatti, U.[Uzair],
Medical Image Segmentation Using Deep Learning
with Feature Enhancement,
IET-IPR(14), No. 14, December 2020, pp. 3324-3332.
DOI Link
2012
BibRef
Bhatti, U.A.[Uzair Aslam],
Liu, J.[Jinru],
Huang, M.X.[Meng-Xing],
Zhang, Y.[Yu],
FF-UNet: Feature fusion based deep learning-powered enhanced
framework for accurate brain tumor segmentation in MRI images,
IVC(161), 2025, pp. 105635.
Elsevier DOI
2509
UNet, CNN, MRI, Tumor segmentation
BibRef
Gagliardi, M.[Marco],
Maurmo, D.[Danilo],
Ruga, T.[Tommaso],
Vocaturo, E.[Eugenio],
Zumpano, E.[Ester],
BrAInVision: A hybrid explainable Artificial Intelligence framework
for brain MRI analysis,
IVC(161), 2025, pp. 105629.
Elsevier DOI
2509
Brain tumor, Machine learning, Hybrid features,
Hand-crafted features, Explainable AI
BibRef
Raheem, A.[Abdul],
Yang, Z.[Zhen],
Manan, M.A.[Malik Abdul],
Ahmed, S.[Shahzad],
Sabah, F.[Fahad],
Adaptive Dual-Model Federated Learning for Generalizable Brain Tumor
Segmentation,
IJIST(35), No. 6, 2025, pp. e70223.
DOI Link
2510
brain tumor, federated learning (FL), medical imaging, MRI, segmentation
BibRef
Shao, M.[Minye],
Wang, Z.[Zeyu],
Duan, H.R.[Hao-Ran],
Huang, Y.W.[Ya-Wen],
Zhai, B.[Bing],
Wang, S.Z.[Shi-Zheng],
Long, Y.[Yang],
Zheng, Y.F.[Ye-Feng],
Rethinking Brain Tumor Segmentation From the Frequency Domain
Perspective,
MedImg(44), No. 11, November 2025, pp. 4536-4553.
IEEE DOI Code:
WWW Link.
2511
Frequency-domain analysis, Tumors, Brain tumors,
Magnetic resonance imaging, Biomedical imaging, Imaging,
multi-modal feature fusion
BibRef
Uppal, D.[Dolly],
Prakash, S.[Surya],
MS2ADM-BTS: Multi-scale Dual Attention Guided Diffusion Model for
Volumetric Brain Tumor Segmentation,
PRL(198), 2025, pp. 115-122.
Elsevier DOI Code:
WWW Link.
2511
Diffusion model, Multi-scale features,
Brain tumor segmentation, Multimodal MRI, Attention
BibRef
Huang, Y.[Yaya],
Liu, L.[Litong],
Zhang, T.Z.[Tian-Zhen],
Wang, S.[Sisi],
Ting, C.M.[Chee-Ming],
Multi-Modal masked autoencoder and parallel Mamba for 3D brain tumor
segmentation,
PRL(199), 2026, pp. 40-46.
Elsevier DOI Code:
WWW Link.
2512
Brain tumor segmentation, Multimodal MRI analysis,
Modality-aware pretraining, State space models, Cross-modal feature fusion
BibRef
Subhashini, K.,
Thangakumar, J.,
Artificial Intelligence-Based Brain Tumor Segmentation Using Adaptive
Hybrid CNN and Classification by Multi-Scale Dilated MobileNet with
Attention Mechanism for MRI Images,
IJIG(26), No. 2, March 2026, pp. 2650006.
DOI Link
2512
BibRef
Chen, W.X.[Wen-Xuan],
Wang, Y.L.[Yu-Lin],
Li, Z.[Zhongsen],
Wang, S.[Shuai],
Wu, S.[Sirui],
Liu, C.[Chuyu],
Fan, Y.H.[Yong-Hong],
Zhao, B.[Benqi],
Zheng, Z.[Zhuozhao],
Shen, D.G.[Ding-Gang],
Song, X.L.[Xiao-Lei],
Multi-Contrast MRI Super-Resolution in Brain Tumors: Arbitrary-Scale
Implicit Sampling and Unsupervised Fine-Tuning,
MedImg(45), No. 4, April 2026, pp. 1395-1406.
IEEE DOI
2604
Magnetic resonance imaging, Training, Adaptation models,
Superresolution, Testing, Data models, Correlation, Transformers,
unsupervised fine-tuning
BibRef
Liu, T.Y.[Tian-Yi],
Jiang, H.[Haochuan],
Huang, K.[Kaizhu],
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor
Segmentation under Incomplete Modalities,
CVPR25(15663-15671)
IEEE DOI Code:
WWW Link.
2508
Hands, Image segmentation, Codes, Magnetic resonance imaging,
Brain tumors, Interference, Brain modeling,
missing modality
BibRef
Zhang, Z.[Zheyu],
Lu, Y.[Yayuan],
Ma, F.P.[Fei-Peng],
Zhang, Y.Y.[Yue-Yi],
Yue, H.[Huanjing],
Sun, X.Y.[Xiao-Yan],
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting
State Space Model,
CVPR25(25982-25992)
IEEE DOI
2508
Image segmentation, Solid modeling, Correlation, Accuracy,
Magnetic resonance imaging, Semantics, Brain tumors, mamba
BibRef
Dhar, J.[Joy],
Zaidi, N.[Nayyar],
Haghighat, M.[Maryam],
Roy, S.[Sudipta],
Goyal, P.[Puneet],
Alavi, A.[Azadeh],
Kumar, V.[Vikas],
Multimodal Fusion Learning with Dual Attention for Medical Imaging,
WACV25(4362-4371)
IEEE DOI Code:
WWW Link.
2505
Attention mechanisms, Uncertainty, Monte Carlo methods,
Magnetic resonance imaging, Brain tumors, Lung cancer, Skin,
applied deep learning
BibRef
Zeng, S.Q.[Shu-Qi],
Liu, J.[Jun],
Xu, J.[Ji],
Luo, Y.[Yuan],
MBTDiff: Multi-Segmentation Brain Tumor Model with Diffusion
Probabilistic Model,
ICIVC24(188-192)
IEEE DOI
2503
Training, Image segmentation, Computational modeling,
Magnetic resonance imaging, Brain tumors, Brain modeling,
deep learning
BibRef
Reddy, D.D.[Divya D.],
Saadat, N.[Niloufar],
Holcomb, J.M.[James M.],
Wagner, B.C.[Benjamin C.],
Truong, N.C.[Nghi C.],
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EnhanceMedIm24(2373-2379)
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2410
Deep learning, Image segmentation, Databases,
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VAND24(3930-3939)
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ICIP22(1301-1305)
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2211
Image segmentation, Magnetic resonance imaging,
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ICPR22(4615-4622)
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Training, Image segmentation, Tensors, Magnetic resonance imaging,
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ICPR22(4420-4426)
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Histopathology, Convolution,
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ICIP22(4208-4212)
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Codes, Brain modeling, Convolutional neural networks, Lesions,
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ICCV21(3955-3964)
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2203
Training, Degradation, Image segmentation, Sensitivity,
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ICIP21(36-40)
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Training, Image segmentation, Brightness, Standards, Tumors,
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A Dense-Gated U-Net for Brain Lesion Segmentation,
VCIP20(104-107)
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biomedical MRI, brain, diseases, feature extraction,
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biomedical MRI, brain, feature extraction, image classification,
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ICIVC21(129-133)
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Training, Degradation, Image segmentation,
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ICIVC20(97-101)
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Image segmentation, Tumors, Decoding,
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ICIP20(2671-2675)
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Tumors, Image segmentation,
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Convolutional Neural Network, Brain Tumor Segmentation,
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biomedical MRI, brain, image segmentation, mutual information,
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ISCV18(1-6)
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1807
biomedical MRI, biothermics, brain, finite difference methods,
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Fully connected CRF with data-driven prior for multi-class brain
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ICIP17(1727-1731)
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biomedical MRI, brain, image segmentation,
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ISIVC16(320-324)
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FCV15(1-4)
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biomedical MRI
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Automated Localization of Brain Tumors in MRI Using Potential-K-Means
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CRV15(125-132)
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Biomedical imaging
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Glioma Detection, Glioblastoma Tumors, Analysis, Brain Glioma .