20.9.1 Brain Tumors, Cortex, Cancer

Ravì, D., Fabelo, H., Callic, G.M., Yang, G.Z.,
Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging,
MedImg(36), No. 9, September 2017, pp. 1845-1857.
IEEE DOI 1709
biological tissues, brain, cancer, hyperspectral imaging, image classification, image segmentation, medical image processing, semantic networks, stochastic processes, T-distributed stochastic neighbor approach, brain surgery, dimensionality reduction scheme, hyperspectral brain imaging, semantic segmentation technique, semantic texton forest, tissue classification, tumor classification map, Brain, Cancer, Hyperspectral imaging, Image segmentation, Manifolds, Semantics, Tumors, Manifold embedding, brain cancer detection, hyperspectral imaging, semantic, segmentation BibRef

Anitha, R., Raja, D.S.S.[D. Siva Sundhara],
Segmentation of glioma tumors using convolutional neural networks,
IJIST(27), No. 4, 2017, pp. 354-360.
DOI Link 1712
brain tumors, classifier, features, glioma, image fusion BibRef

Rundo, L.[Leonardo], Militello, C.[Carmelo], Tangherloni, A.[Andrea], Russo, G.[Giorgio], Vitabile, S.[Salvatore], Gilardi, M.C.[Maria Carla], Mauri, G.[Giancarlo],
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique,
IJIST(28), No. 1, 2018, pp. 21-37.
DOI Link 1802
brain tumors, magnetic resonance imaging, necrosis extraction, neuro-radiosurgery treatments, unsupervised Fuzzy C-Means clustering BibRef

Ding, Y.[Yi], Dong, R.F.[Rong-Feng], Lan, T.[Tian], Li, X.R.[Xue-Rui], Shen, G.Y.[Guang-Yu], Chen, H.[Hao], Qin, Z.[Zhiguang],
Multi-modal brain tumor image segmentation based on SDAE,
IJIST(28), No. 1, 2018, pp. 38-47.
DOI Link 1802
brain tumor segmentation, BRATS 2015, stacked de-noising auto-encoder BibRef

Anitha, R., Raja, D.S.S.[D. Siva Sundhara],
Development of computer-aided approach for brain tumor detection using random forest classifier,
IJIST(28), No. 1, 2018, pp. 48-53.
DOI Link 1802
abnormal patterns, brain tumors, classification, diagnose, segmentation BibRef

Sasikanth, S., Kumar, S.S.[S. Suresh],
Glioma tumor detection in brain MRI image using ANFIS-based normalized graph cut approach,
IJIST(28), No. 1, 2018, pp. 64-71.
DOI Link 1802
classifier, glioma tumor, graph cut approach, orientation analysis, validation BibRef

Wu, G.Q.[Guo-Qing], Chen, Y.S.[Yin-Sheng], Wang, Y.Y.[Yuan-Yuan], Yu, J.H.[Jin-Hua], Lv, X.F.[Xiao-Fei], Ju, X.[Xue], Shi, Z.F.[Zhi-Feng], Chen, L.[Liang], Chen, Z.P.[Zhong-Ping],
Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors,
MedImg(37), No. 4, April 2018, pp. 893-905.
IEEE DOI 1804
Cancer, Dictionaries, Estimation, Feature extraction, Medical diagnostic imaging, Tumors, Brain tumors, tumor differentiation BibRef

Anantha, N.H.[N. Herald], Selvathi, R.D.[Rufus D.],
Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier,
IJIST(28), No. 2, 2018, pp. 77-85.
WWW Link. 1806
BibRef

Pinto, A.[Adriano], Pereira, S.[Sérgio], Rasteiro, D.[Deolinda], Silva, C.A.[Carlos A.],
Hierarchical brain tumour segmentation using extremely randomized trees,
PR(82), 2018, pp. 105-117.
Elsevier DOI 1806
Brain tumour, Magnetic resonance imaging, Image segmentation, Hierarchy of classifiers, Extremely randomized trees, Machine learning BibRef

Izadyyazdanabadi, M.[Mohammadhassan], Belykh, E.[Evgenii], Mooney, M.[Michael], Martirosyan, N.[Nikolay], Eschbacher, J.[Jennifer], Nakaji, P.[Peter], Preul, M.C.[Mark C.], Yang, Y.Z.[Ye-Zhou],
Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images,
JVCIR(54), 2018, pp. 10-20.
Elsevier DOI 1806
Neural network, Unsupervised localization, Ensemble modeling, Brain tumor, Confocal laser endomicroscopy, Surgical vision BibRef

Arnaud, A., Forbes, F., Coquery, N., Collomb, N., Lemasson, B., Barbier, E.L.,
Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI Data,
MedImg(37), No. 7, July 2018, pp. 1678-1689.
IEEE DOI 1808
biomedical MRI, brain, cancer, feature extraction, Gaussian distribution, image segmentation, fingerprint model BibRef

Ma, C., Luo, G., Wang, K.,
Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images,
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 BibRef

Tang, Z., Ahmad, S., Yap, P., Shen, D.,
Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery,
MedImg(37), No. 10, October 2018, pp. 2224-2235.
IEEE DOI 1810
Brain, Tumors, Image segmentation, Pathology, Convergence, Radiology, Low-rank, multi-atlas segmentation BibRef

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 tumour segmentation,
IJIST(28), No. 4, December 2018, pp. 254-266.
WWW Link. 1811
BibRef

Selvapandian, A., Manivannan, K.,
Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier,
IJIST(28), No. 4, December 2018, pp. 295-301.
WWW Link. 1811
BibRef

Lian, C., Ruan, S., Denœux, T., Li, H., Vera, P.,
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions,
IP(28), No. 2, February 2019, pp. 755-766.
IEEE DOI 1811
cancer, computerised tomography, image fusion, image segmentation, iterative methods, lung, medical image processing, PET-CT BibRef

Chen, S.C.[Sheng-Cong], Ding, C.X.[Chang-Xing], Liu, M.F.[Min-Feng],
Dual-force convolutional neural networks for accurate brain tumor segmentation,
PR(88), 2019, pp. 90-100.
Elsevier DOI 1901
Brain tumor segmentation, Dual-force network, Convolutional neural network, Label distribution, Post-processing BibRef

Chang, J.[Jie], Zhang, L.[Luming], Gu, N.[Naijie], Zhang, X.[Xiaoci], Ye, M.[Minquan], Yin, R.[Rongzhang], 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

Rezaei, K.[Kimia], Agahi, H.[Hamed], Mahmoodzadeh, A.[Azar],
Multi-objective differential evolution-based ensemble method for brain tumour diagnosis,
IET-IPR(13), No. 9, 18 July 2019, pp. 1421-1430.
DOI Link 1907
BibRef

Lipková, J., Angelikopoulos, P., Wu, S., Alberts, E., Wiestler, B., Diehl, C., Preibisch, C., Pyka, T., Combs, S.E., Hadjidoukas, P., van Leemput, K., Koumoutsakos, P., Lowengrub, J., Menze, B.,
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference,
MedImg(38), No. 8, August 2019, pp. 1875-1884.
IEEE DOI 1908
Tumors, Mathematical model, Bayes methods, Biomedical imaging, Magnetic resonance imaging, Predictive models, Glioblastoma, multimodal medical scans BibRef

Rajagopal, R.,
Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features,
IJIST(29), No. 3, September 2019, pp. 353-359.
DOI Link 1908
BibRef

Kale, V.V.[Vandana V.], Hamde, S.T.[Satish T.], Holambe, R.S.[Raghunath S.],
Brain disease diagnosis using local binary pattern and steerable pyramid,
MultInfoRetr(8), No. 3, September 2019, pp. 155-165.
Springer DOI 1908
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

Nagarathinam, E.[Ezhilmathi], Ponnuchamy, T.[Thirumurugan],
Image registration-based brain tumor detection and segmentation using ANFIS classification approach,
IJIST(29), No. 4, 2019, pp. 510-517.
DOI Link 1911
abnormal cells, classifications, detection, segmentation, tumor BibRef

Johnpeter, J.H.[Jasmine Hephzipah], Ponnuchamy, T.[Thirumurugan],
Computer aided automated detection and classification of brain tumors using CANFIS classification method,
IJIST(29), No. 4, 2019, pp. 431-438.
DOI Link 1911
abnormal, brain, classification, statistical features, tumors 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

Tamilmani, G., Sivakumari, S.,
Early detection of brain cancer using association allotment hierarchical clustering,
IJIST(29), No. 4, 2019, pp. 617-632.
DOI Link 1911
association allotment hierarchical clustering, gray wolf optimization, mutual piece-wise linear transformation filtering BibRef

Meng, H., Wang, K., Gao, Y., Jin, Y., Ma, X., Tian, J.,
Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography,
MedImg(38), No. 12, December 2019, pp. 2726-2734.
IEEE DOI 1912
Fluorescence, Image reconstruction, Imaging, In vivo, Kernel, Probes, Tumors, Fluorescence tomography, multi-modality fusion, brain BibRef

Sharif, M.[Muhammad], Amin, J.[Javaria], Raza, M.[Mudassar], Yasmin, M.[Mussarat], Satapathy, S.C.[Suresh Chandra],
An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor,
PRL(129), 2020, pp. 150-157.
Elsevier DOI 2001
BSE, PSO, GA, LBP, Deep features, ANN 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 recognition of brain tumors using MRI images,
PRL(129), 2020, pp. 181-189.
Elsevier DOI 2001
Brain tumor, Contrast improvement, Deep saliency method, Features extraction, Optimization, Recognition BibRef

Mahesh, K.M.[K. Michael], Renjit, J.A.[J. Arokia],
Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images,
IJIST(30), No. 1, 2020, pp. 234-251.
DOI Link 2002
deep convolutional neural networks, Jaya optimization algorithm, multimodal MRI brain images, severity-level classification 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

Peng, S.[Suting], Chen, W.[Wei], Sun, J.W.[Jia-Wei], Liu, B.[Boqiang],
Multi-Scale 3D U-Nets: An approach to automatic segmentation of brain tumor,
IJIST(30), No. 1, 2020, pp. 5-17.
DOI Link 2002
brain tumor, CNN, deep learning, gliomas, image segmentation 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

Nasor, M.[Mohamed], Obaid, W.[Walid],
Detection and localisation of multiple brain tumours by object counting and elimination,
IET-IPR(14), No. 4, 27 March 2020, pp. 615-620.
DOI Link 2003
BibRef

Goceri, E.[Evgin],
CapsNet topology to classify tumours from brain images and comparative evaluation,
IET-IPR(14), No. 5, 17 April 2020, pp. 882-889.
DOI Link 2004
BibRef

Tiwari, A.[Arti], Srivastava, S.[Shilpa], Pant, M.[Millie],
Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019,
PRL(131), 2020, pp. 244-260.
Elsevier DOI 2004
Brain tumor segmentation, Glioma, Neoplasia, Magnetic resonance imaging (MRI) BibRef

Zhang, W.X.[Wen-Xue], Jian, J.B.[Jian-Bo], Sun, C.[Cuiyun], Chen, J.[Jie], Lv, W.J.[Wen-Juan], Sun, M.Y.[Meng-Yu], Zhao, Y.Q.[Yu-Qing], Zhao, Q.[Qi], Hu, C.H.[Chun-Hong],
High-resolution 3D imaging of microvascular architecture in human glioma tissues using X-ray phase-contrast computed tomography as a potential adjunct to histopathology,
IJIST(30), No. 2, 2020, pp. 464-472.
DOI Link 2005
glioma, microthrombi, microvascular architecture, X-ray phase-contrast computed tomography BibRef

Lu, Z.Y.[Zhen-Yu], Bai, Y.Z.[Yan-Zhong], Chen, Y.[Yi], Su, C.Q.[Chun-Qiu], Lu, S.S.[Shan-Shan], Zhan, T.M.[Tian-Ming], Hong, X.N.[Xun-Ning], Wang, S.H.[Shui-Hua],
The classification of gliomas based on a Pyramid dilated convolution resnet model,
PRL(133), 2020, pp. 173-179.
Elsevier DOI 2005
Gliomas, Classification, Deep learning, ResNet, Dilated convolution 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

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

Liu, X.M.[Xiao-Ming], Zhou, X.B.[Xiao-Bo], Qian, X.H.[Xiao-Hua],
Transparency-guided ensemble convolutional neural network for the stratification between pseudoprogression and true progression of glioblastoma multiform in MRI,
JVCIR(72), 2020, pp. 102880.
Elsevier DOI 2010
Pseudo progression, Glioblastoma multiforme, Diffusion tensor imaging (DTI), Ensemble CNN 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

Zhang, D.W.[Ding-Wen], Huang, G.H.[Guo-Hai], Zhang, Q.A.[Qi-Ang], Han, J.G.[Jun-Gong], Han, J.W.[Jun-Wei], Yu, Y.Z.[Yi-Zhou],
Cross-modality deep feature learning for brain tumor segmentation,
PR(110), 2021, pp. 107562.
Elsevier DOI 2011
Brain tumor segmentation, Cross-modality feature transition, Cross-modality feature fusion, Feature learning 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

Leena, B.[Bojaraj], Jayanthi, A.[Annamalai],
Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification,
IJIST(30), No. 4, 2020, pp. 874-898.
DOI Link 2011
brain tumor classification, feature extraction, optimization, segmentation, skull stripping BibRef

Kalaiselvi, T.[Thiruvenkadam], Padmapriya, T.[Thiyagarajan], Sriramakrishnan, P.[Padmanaban], Priyadharshini, V.[Venugopal],
Development of automatic glioma brain tumor detection system using deep convolutional neural networks,
IJIST(30), No. 4, 2020, pp. 926-938.
DOI Link 2011
BraTS, deep learning, glioma tumor, neural networks, tumor detection, WBA 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, Computer architecture, 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

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

Nwe, T.L., Min, O.Z., Gopalakrishnan, S., Lin, D., Prasad, S., Dong, S., Li, Y., Pahwa, R.S.,
Improving 3D Brain Tumor Segmentation With Predict-Refine Mechanism Using Saliency And Feature Maps,
ICIP20(2671-2675)
IEEE DOI 2011
Tumors, Image segmentation, Magnetic resonance imaging, Training, Brain modeling, Brain tumor Segmentation BibRef

El Kaitouni, S.E.I., Tairi, H.,
Segmentation of medical images for the extraction of brain tumors: A comparative study between the Hidden Markov and Deep Learning approaches,
ISCV20(1-5)
IEEE DOI 2011
biomedical MRI, brain, feature extraction, image classification, image segmentation, learning (artificial intelligence), Medical images. BibRef

Li, K., Kong, L., Zhang, Y.,
3D U-Net Brain Tumor Segmentation Using VAE Skip Connection,
ICIVC20(97-101)
IEEE DOI 2009
Image segmentation, Tumors, Decoding, Magnetic resonance imaging, Semantics, Computer architecture, ShakeDrop BibRef