21.9.1 Brain Tumors, Cortex, Cancer

Chapter Contents (Back)
Brain. Brain Tumor. Tumor.
See also Brain Tumor Detection, MRI Data.
See also Glioma Detection, Glioblastoma Tumors, Analysis, Brain Glioma.
See also Survival Analysis, Cancer Survival.

Brain Lesion Segmentation,
Online
WWW Link. 1605
Code, Brain Lesion Segmentation. multi-scale 3D Deep Convolutional Neural Network coupled with a 3D fully connected Conditional Random Field. BibRef

Corso, J.J.[Jason J.], Sharon, E.[Eitan], Dube, S.[Shishir], El-Saden, S.[Suzie], Sinha, U.[Usha], Yuille, A.L.[Alan L.],
Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification,
MedImg(27), No. 5, May 2008, pp. 629-640.
IEEE DOI 0711
BibRef

Al-Kadi, O.S.[Omar S.],
Texture measures combination for improved meningioma classification of histopathological images,
PR(43), No. 6, June 2010, pp. 2043-2053.
Elsevier DOI 1003
BibRef
Earlier:
A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours,
ICIP09(4177-4180).
IEEE DOI 0911
Coloured texture analysis; Feature extraction; Histopathological images; Meningioma; Naive Bayesian classifier; Bhattacharyya distance BibRef

Jayachandran, A., Dhanasekaran, R.,
Brain tumor severity analysis using modified multi-texton histogram and hybrid kernel SVM,
IJIST(24), No. 1, 2014, pp. 72-82.
DOI Link 1403
tumor BibRef

Angoth, V.[Vivek], Dwith, C.Y.N., Singh, A.[Amarjot],
A Novel Wavelet Based Image Fusion for Brain Tumor Detection,
IJCVSP(2), No. 1, 2013, pp. xx-yy.
WWW Link. 1303
BibRef

Dhanalakshmi, K., Rajamani, V.,
An intelligent mining system for diagnosing medical images using combined texture-histogram features,
IJIST(23), No. 2, 2013, pp. 194-203.
DOI Link brain tumor, image processing, association rule mining, associative classifier 1307
BibRef

Arakeri, M.P.[Megha P.], Reddy, G.R.M.[G. Ram Mohana],
An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis,
MultInfoRetr(2), No. 3, September 2013, pp. 175-188.
WWW Link. 1307
BibRef

Vilamala, A.[Albert], Lisboa, P.J.G.[Paulo J.G.], Ortega-Martorell, S.[Sandra], Vellido, A.[Alfredo],
Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours,
PRL(34), No. 14, 2013, pp. 1734-1747.
Elsevier DOI 1308
Discriminant Convex Non-negative Matrix Factorization BibRef

Govindaraj, V.[Vishnuvarthanan], Murugan, P.R.[Pallikonda Rajasekaran],
A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques,
IJIST(24), No. 4, 2014, pp. 313-325.
DOI Link 1411
image segmentation BibRef

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X.T.[Xiao-Tao], Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R.[T. Riklin], Reza, S.M.S., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C.[Hoo-Chang], Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H.[Dong Hye], Zhao, L.[Liang], Zhao, B.[Binsheng], Zikic, D., Prastawa, M., Reyes, M., van Leemput, K.,
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),
MedImg(34), No. 10, October 2015, pp. 1993-2024.
IEEE DOI 1511
benchmark testing BibRef

Shanthakumar, P., Kumar, P.G.[P. Ganesh],
Computer aided brain tumor detection system using watershed segmentation techniques,
IJIST(25), No. 4, 2015, pp. 297-301.
DOI Link 1512
enhancement BibRef

Stefano, A.[Alessandro], Vitabile, S.[Salvatore], Russo, G.[Giorgio], Ippolito, M.[Massimo], Marletta, F.[Franco], d'Arrigo, C.[Corrado], d'Urso, D.[Davide], Gambino, O.[Orazio], Pirrone, R.[Roberto], Ardizzone, E.[Edoardo], Gilardi, M.C.[Maria Carla],
A fully automatic method for biological target volume segmentation of brain metastases,
IJIST(26), No. 1, 2016, pp. 29-37.
DOI Link 1604
random walk BibRef

Kumarganesh, S., Suganthi, M.,
An efficient approach for brain image (tissue) compression based on the position of the brain tumor,
IJIST(26), No. 4, 2016, pp. 237-242.
DOI Link 1701
brain tumor BibRef

Vishnuvarthanan, G., Rajasekaran, M.P.[M. Pallikonda], Vishnuvarthanan, N.A.[N. Anitha], Prasath, T.A.[T. Arun], Kannan, M.,
Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed-based region growing algorithm,
IJIST(27), No. 1, 2017, pp. 33-45.
DOI Link 1704
MPSO-based FCM BibRef

Angulakshmi, M., Priya, G.G.L.[G.G. Lakshmi],
Automated brain tumour segmentation techniques: A review,
IJIST(27), No. 1, 2017, pp. 66-77.
DOI Link 1704
review BibRef

Ramakrishnan, T., Sankaragomathi, B.,
A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation,
PRL(94), No. 1, 2017, pp. 163-171.
Elsevier DOI 1708
Feature, extraction BibRef

Rufus, N.H.A.[N. Herald Anantha], Selvathi, D.,
Performance analysis of computer aided brain tumor detection system using ANFIS classifier,
IJIST(27), No. 3, 2017, pp. 273-280.
DOI Link 1708
brain image, classifier, features, GLCM, , tumor 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.G.[Zhi-Guang],
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

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

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

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

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

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

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

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

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

Hu, J.Y.[Jing-Yu], Gu, X.J.[Xiao-Jing], Gu, X.S.[Xing-Sheng],
Dual-pathway DenseNets with fully lateral connections for multimodal brain tumor segmentation,
IJIST(31), No. 1, 2021, pp. 364-378.
DOI Link 2102
brain tumor, convolutional network, dense network, multimodalities, segmentation BibRef

Yu, B.T.[Bi-Ting], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Yang, W.Q.[Wan-Qi], Yang, M.[Ming], Bourgeat, P.[Pierrick], Fripp, J.[Jurgen],
SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for Brain Tumor Segmentation,
MedImg(40), No. 5, May 2021, pp. 1417-1427.
IEEE DOI 2105
Image segmentation, Table lookup, Tumors, Task analysis, Solid modeling, neural network BibRef

Jeevanantham, V., MohanBabu, G.,
Detection and diagnosis of brain tumors-framework using extreme machine learning and CANFIS classification algorithms,
IJIST(31), No. 2, 2021, pp. 540-547.
DOI Link 2105
brain, features, machine learning, transforms, tumors BibRef

Hu, A.[An], Razmjooy, N.[Navid],
Brain tumor diagnosis based on metaheuristics and deep learning,
IJIST(31), No. 2, 2021, pp. 657-669.
DOI Link 2105
brain tumor, deep belief network, feature extraction, feature selection, classification, improved seagull optimization algorithm BibRef

Dzulkifli, F.A.[Fahmi Akmal], Mashor, M.Y.[Mohd Yusoff], Jaafar, H.[Hasnan],
Colour thresholding-based automatic Ki67 counting procedure for Immunohistochemical staining in meningioma,
IJCVR(11), No. 3, 2021, pp. 279-298.
DOI Link 2106
BibRef

Bagyaraj, S.[Sanjeevirayar], Tamilselvi, R.[Rajendran], Gani, P.B.M.[Parisa Beham Mohamed], Sabarinathan, D.[Devanathan],
Brain tumour cell segmentation and detection using deep learning networks,
IET-IPR(15), No. 10, 2021, pp. 2363-2371.
DOI Link 2108
BibRef

Alaraimi, S.[Saleh], Okedu, K.E.[Kenneth E.], Tianfield, H.[Hugo], Holden, R.[Richard], Uthmani, O.[Omair],
Transfer learning networks with skip connections for classification of brain tumors,
IJIST(31), No. 3, 2021, pp. 1564-1582.
DOI Link 2108
AlexNet, convolutional neural network (CNN), deep learning, GoogLeNet, transfer learning, VGG BibRef

Deepak, S., Ameer, P.M.,
Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space,
IJIST(31), No. 3, 2021, pp. 1655-1669.
DOI Link 2108
brain tumour, classification, Mahalanobis distance, neighbourhood, Siamese networks BibRef

Gurunathan, A.[Akila], Krishnan, B.[Batri],
Detection and diagnosis of brain tumors using deep learning convolutional neural networks,
IJIST(31), No. 3, 2021, pp. 1174-1184.
DOI Link 2108
brain, deep learning, machine learning, segmentation, tumors BibRef

Abdelaziz, M.[Mohammed], Cherfa, Y.[Yazid], Cherfa, A.[Assia], Alim-Ferhat, F.[Fatiha],
Automatic brain tumor segmentation for a computer-aided diagnosis system,
IJIST(31), No. 4, 2021, pp. 2226-2236.
DOI Link 2112
3D reconstruction, Graph Cut, Level Set, Random Forest, segmentation BibRef

Liu, T.T.[Ting-Ting], Yuan, Z.[Zhi], Wu, L.[Li], Badami, B.[Benjamin],
Retraction: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm,
IJIST(34), No. 2, 2024, pp. e23038.
DOI Link 2402
Was: BibRef IJIST(31), No. 4, 2021, pp. 1921-1935. 2112
BibRef

Latif, U.[Urva], Shahid, A.R.[Ahmad R.], Raza, B.[Basit], Ziauddin, S.[Sheikh], Khan, M.A.[Muazzam A.],
An end-to-end brain tumor segmentation system using multi-inception-UNET,
IJIST(31), No. 4, 2021, pp. 1803-1816.
DOI Link 2112
brain tumor, BRATS, CNN, inception, UNET BibRef

Siar, M.[Masoumeh], Teshnehlab, M.[Mohammad],
A combination of feature extraction methods and deep learning for brain tumour classification,
IET-IPR(16), No. 2, 2022, pp. 416-441.
DOI Link 2201
BibRef

Adu, K.[Kwabena], Yu, Y.B.[Yong-Bin], Cai, J.Y.[Jing-Ye], Asare, I.[Isaac], Quahin, J.[Jennifer],
The influence of the activation function in a capsule network for brain tumor type classification,
IJIST(32), No. 1, 2022, pp. 123-143.
DOI Link 2201
activation function, brain tumor classification, capsule network, convolutional neural network, deep learning BibRef

Anjum, S.[Sadia], Hussain, L.[Lal], Ali, M.[Mushtaq], Alkinani, M.H.[Monagi H.], Aziz, W.[Wajid], Gheller, S.[Sabrina], Abbasi, A.A.[Adeel Ahmed], Marchal, A.R.[Ali Raza], Suresh, H.[Harshini], Duong, T.Q.[Tim Q.],
Detecting brain tumors using deep learning convolutional neural network with transfer learning approach,
IJIST(32), No. 1, 2022, pp. 307-323.
DOI Link 2201
brain tumor, convolution neural network, decision tree, deep learning BibRef

Cui, S.G.[Shao-Guo], Wei, M.J.[Ming-Jun], Liu, C.[Chang], Jiang, J.F.[Jing-Feng],
GAN-segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation,
IJIST(32), No. 3, 2022, pp. 857-868.
DOI Link 2205
autoencoder, brain tumor, generative adversarial network, label imbalance, semantic segmentation BibRef

Ezhov, I.[Ivan], Mot, T.[Tudor], Shit, S.[Suprosanna], Lipkova, J.[Jana], Paetzold, J.C.[Johannes C.], Kofler, F.[Florian], Pellegrini, C.[Chantal], Kollovieh, M.[Marcel], Navarro, F.[Fernando], Li, H.W.[Hong-Wei], Metz, M.[Marie], Wiestler, B.[Benedikt], Menze, B.[Bjoern],
Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models,
MedImg(41), No. 5, May 2022, pp. 1269-1278.
IEEE DOI 2205
Tumors, Numerical models, Computational modeling, Brain modeling, Mathematical models, Biological system modeling, Bayes methods, FET-PET BibRef

Zhou, T.X.[Tong-Xue], Vera, P.[Pierre], Canu, S.[Stéphane], Ruan, S.[Su],
Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation,
PRL(158), 2022, pp. 125-132.
Elsevier DOI 2205
Brain tumor segmentation, Conditional generator, Correlation learning, Missing data, Multimodal fusion BibRef

Zhou, T.X.[Tong-Xue],
Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning,
PR(149), 2024, pp. 110282.
Elsevier DOI 2403
Brain tumor segmentation, Multi-modal feature fusion, Disentangled representation learning, Contrastive learning BibRef

Polat, Ö.[Özlem], Dokur, Z.[Zümray], Ölmez, T.[Tamer],
Brain tumor classification by using a novel convolutional neural network structure,
IJIST(32), No. 5, 2022, pp. 1646-1660.
DOI Link 2209
brain tumors, classification, convolutional neural networks, divergence analysis, pattern recognition BibRef

Joseph, S.S.[Sushitha Susan], Dennisan, A.[Aju],
An affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG-DMCA) based three dimensional image reconstruction approach,
IJIST(32), No. 6, 2022, pp. 2240-2254.
DOI Link 2212
3D reconstruction technique, brain tumor, deep learned marching cubes algorithm, Shapelets BibRef

Lather, M.[Mansi], Singh, P.[Parvinder],
DDVM: dual decision voting mechanism for brain tumour identification with LBP2Q-SVM type classifier,
IJCVR(13), No. 1, 2023, pp. 52-72.
DOI Link 2212
BibRef

Khosravanian, A.[Asieh], Rahmanimanesh, M.[Mohammad], Keshavarzi, P.[Parviz], Mozaffari, S.[Saeed],
Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning,
IJIST(33), No. 1, 2023, pp. 323-339.
DOI Link 2301
brain tumor segmentation, deep learning, fuzzy C-means clustering, level set method, shape prior information BibRef

Li, Z.W.[Zi-Wei], Xuan, S.B.[Shi-Bin], He, X.D.[Xue-Dong], Wang, L.[Li],
Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation,
IET-IPR(17), No. 2, 2023, pp. 418-427.
DOI Link 2302
BibRef

Kavitha, A.R.[Angamuthu Rajasekaran], Palaniappan, K.[Karthikeyan],
Brain tumor segmentation using a deep Shuffled-YOLO network,
IJIST(33), No. 2, 2023, pp. 511-522.
DOI Link 2303
brain tumor, multi-modalities, scalable range-based adaptive bilateral filter, segmentation, Shuffled-YOLO network BibRef

Raju, A.R.[Ayalapogu Ratna], Pabboju, S.[Suresh], Ramisetty, R.R.[Rajeswara Rao],
Performance Analysis and Critical Review on Segmentation Techniques for Brain Tumor Classification,
IJIG(23), No. 2 2023, pp. 2350023.
DOI Link 2303
BibRef

Liu, Z.X.[Zeng-Xin], Ma, C.W.[Cai-Wen], She, W.J.[Wen-Ji], Wang, X.[Xuan],
TransMVU: Multi-view 2D U-Nets with transformer for brain tumour segmentation,
IET-IPR(17), No. 6, 2023, pp. 1874-1882.
DOI Link 2305
image segmentation, medical image processing, tumours BibRef

Zhou, T.X.[Tong-Xue],
Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network,
PR(141), 2023, pp. 109665.
Elsevier DOI 2306
Brain tumor segmentation, Multimodal feature fusion, Missing modalities, Spatial consistency, Latent feature learning BibRef

Xue, J.[Jie], Li, Q.[Qi], Liu, X.[Xiyu], Guo, Y.J.[Yu-Jie], Lu, J.[Jie], Song, B.S.[Bo-Sheng], Huang, P.[Pu], An, Q.[Qiong], Gong, G.Z.[Guan-Zhong], Li, D.W.[Deng-Wang],
Hybrid neural-like P systems with evolutionary channels for multiple brain metastases segmentation,
PR(142), 2023, pp. 109651.
Elsevier DOI 2307
Hybrid neural-like P system, Evolutionary channels, Segmentation of brain metastases BibRef

Nehru, V., Prabhu, V.,
Segmentation of brain tumor subregions with depthwise separable dense U-NET (DSDU-NET),
IJIST(33), No. 4, 2023, pp. 1323-1334.
DOI Link 2307
brain tumor segmentation, depthwise separable convolutional networks, whole tumor (WT) BibRef

Zia, M.S.[Muhammad Sultan], Baig, U.A.[Usman Ali], Rehman, Z.U.[Zaka Ur], Yaqub, M.[Muhammad], Ahmed, S.[Shahzad], Zhang, Y.D.[Yu-Dong], Wang, S.[Shui=Hua], Khan, R.[Rizwan],
Contextual information extraction in brain tumour segmentation,
IET-IPR(17), No. 12, 2023, pp. 3371-3391.
DOI Link 2310
attention gate, attentional residual dropout block, context aware 3D ARDUNet, convolutional neural networks, residual dropout block BibRef

Das, P.[Poulomi], Das, A.[Arpita],
Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors,
PR(144), 2023, pp. 109879.
Elsevier DOI 2310
Advanced PCNN module, Classification, FCM clustering algorithm, Interlayer feature quantifiers, NSST based decomposition BibRef

Li, Q.[Qiang], Liu, H.X.[Heng-Xin], Nie, W.Z.[Wei-Zhi], Wu, T.[Ting],
Brain tumor image segmentation based on prior knowledge via transformer,
IJIST(33), No. 6, 2023, pp. 2073-2087.
DOI Link 2311
attention mechanism, brain tumor segmentation, prior knowledge, transformer BibRef

Sultana, T.[Tania], Kurosaki, S.[Sho], Jitsumatsu, Y.[Yutaka], Kuhara, S.[Shigehide], Takeuchi, J.[Jun'ichi],
Brain Tumor Classification using Under-Sampled k-Space Data: A Deep Learning Approach,
IEICE(E106-D), No. 11, November 2023, pp. 1831-1841.
WWW Link. 2311
BibRef

Khushi, H.M.T.[Hafiz Muhammad Tayyab], Masood, T.[Tehreem], Jaffar, A.[Arfan], Akram, S.[Sheeraz], Bhatti, S.M.[Sohail Masood],
Performance analysis of state-of-the-art CNN architectures for brain tumour detection,
IJIST(34), No. 1, 2024, pp. e22949.
DOI Link 2401
artificial intelligence, Br35h, brain tumour, deep learning, machine learning, medical image analysis BibRef

Raza, A.[Asif], Alshehri, M.S.[Mohammed S.], Almakdi, S.[Sultan], Siddique, A.A.[Ali Akbar], Alsulami, M.[Mohammad], Alhaisoni, M.[Majed],
Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection,
IJIST(34), No. 1, 2024, pp. e22957.
DOI Link 2401
brain tumor classification, deep learning, DenseNet-121, Inception V3, transfer learning BibRef

Soni, V.[Vaibhav], Singh, N.K.[Nikhil Kumar], Singh, R.K.[Rishi Kumar], Tomar, D.S.[Deepak Singh],
Multiencoder-based federated intelligent deep learning model for brain tumor segmentation,
IJIST(34), No. 1, 2024, pp. e22981.
DOI Link 2401
artificial intelligent dilated convolution, brain tumor segmentation, channel attention, multi-encoder BibRef

Liu, H.[Huabing], Ni, Z.Z.[Zheng-Ze], Nie, D.[Dong], Shen, D.G.[Ding-Gang], Wang, J.[Jinda], Tang, Z.Y.[Zhen-Yu],
Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images,
IP(33), 2024, pp. 1199-1210.
IEEE DOI Code:
WWW Link. 2402
Tumors, Brain, Image segmentation, Convolution, Correlation, Lesions, Image reconstruction, Brain tumor segmentation, BraTS2022 dataset BibRef

Kumar, S.[Sangeet], Biswal, B.,
MAEU-NET: A novel supervised architecture for brain tumor segmentation,
IJIST(34), No. 2, 2024, pp. e22988.
DOI Link 2402
brain tumor, context aggregation, MAEU-net, parallel pooling module, receptive field strength, segmentation BibRef

Aggarwal, M.[Meenakshi], Khullar, V.[Vikas], Goyal, N.[Nitin], Rastogi, R.[Rashi], Singh, A.[Aman], Torres, V.Y.[Vanessa Yelamos], Albahar, M.A.[Marwan Ali],
Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification,
IJIST(34), No. 2, 2024, pp. e22994.
DOI Link 2402
brain tumor, convolutional neural network, deep learning, federated learning, independent and identically distributed, transfer learning BibRef

Mehmood, Y.[Yasar], Bajwa, U.I.[Usama Ijaz], Anwar, M.W.[Muhammad Waqas],
Brain tumor grade classification using multi-step pre-training,
IJIST(34), No. 2, 2024, pp. e23008.
DOI Link 2402
brain tumor, computational efficiency, domain adaptive pre-training, transfer learning BibRef

Dheepak, G., Christaline, J.A.[J. Anita], Vaishali, D.,
MEHW-SVM multi-kernel approach for improved brain tumour classification,
IET-IPR(18), No. 4, 2024, pp. 856-874.
DOI Link 2403
brain tumours, global grey level co-occurrence matrix (GLCM), local binary patterns (LBP), principal component analysis (PCA) BibRef

Chen, R.L.[Run-Lin], Lin, Y.P.[Yang-Ping], Ren, Y.M.[Yan-Ming], Deng, H.[Hao], Cui, W.Y.[Wen-Yao], Liu, W.J.[Wen-Jie],
An efficient brain tumor segmentation model based on group normalization and 3D U-Net,
IJIST(34), No. 3, 2024, pp. e23072.
DOI Link 2404
attention, brain tumor, efficient, group normalization, segmentation BibRef

Xu, Y.[Yang], Yu, K.[Kun], Qi, G.Q.[Guan-Qiu], Gong, Y.F.[Yi-Fei], Qu, X.L.[Xiao-Long], Yin, L.[Li], Yang, P.[Pan],
Brain tumour segmentation framework with deep nuanced reasoning and Swin-T,
IET-IPR(18), No. 6, 2024, pp. 1550-1564.
DOI Link Code:
WWW Link. 2405
image segmentation, medical image processing BibRef

Zhou, L.F.[Li-Fang], Jiang, Y.[Yu], Li, W.S.[Wei-Sheng], Hu, J.[Jun], Zheng, S.[Shenhai],
Shape-Scale Co-Awareness Network for 3D Brain Tumor Segmentation,
MedImg(43), No. 7, July 2024, pp. 2495-2508.
IEEE DOI Code:
WWW Link. 2407
Tumors, Shape, Image segmentation, Brain modeling, Transformers, Convolution, Brain tumor segmentation, shape-aware, transformer, MLP, multi-scale BibRef

Praveena, M., Rao, M.K.[M. Kameswara],
Hybrid Segmentation Approach for Tumors Detection in Brain Using Machine Learning Algorithms,
IJIG(24), No. 5, September 2024, pp. 2340008.
DOI Link 2410
BibRef

Gros, R.[Romane], Rodríguez-Núñez, O.[Omar], Felger, L.[Leonard], Moriconi, S.[Stefano], McKinley, R.[Richard], Pierangelo, A.[Angelo], Novikova, T.[Tatiana], Vassella, E.[Erik], Schucht, P.[Philippe], Hewer, E.[Ekkehard], Maragkou, T.[Theoni],
Characterization of Polarimetric Properties in Various Brain Tumor Types Using Wide-Field Imaging Mueller Polarimetry,
MedImg(43), No. 12, December 2024, pp. 4120-4132.
IEEE DOI 2412
Tumors, Imaging, Brain, Polarimetry, Optical polarization, Optical imaging, Heterojunction bipolar transistors, neuro-oncology BibRef

Zhou, D.M.[Dong-Mei], Luo, H.[Hao], Li, X.Y.[Xing-Yang], Chen, S.B.[Sheng-Bing],
HRGUNet: A novel high-resolution generative adversarial network combined with an improved UNet method for brain tumor segmentation,
JVCIR(105), 2024, pp. 104345.
Elsevier DOI 2501
Computer-aided segmentation, Segmentation network, Brain tumor, High-resolution, Generative adversarial networks BibRef

Abadian-Zadeh, F.S.[Fatemeh-Sadat], Mohammadi, M.R.[Mohammad Reza], Soryani, M.[Mohsen],
Weakly supervised brain tumour segmentation with label propagation and level set loss,
IET-IPR(19), No. 1, 2025, pp. e13289.
DOI Link 2501
image segmentation, learning (artificial intelligence), medical image processing, neural net architecture, tumours BibRef

Fu, J.[Jia], Wang, G.[Guotai], Lu, T.[Tao], Yue, Q.[Qiang], Vercauteren, T.[Tom], Ourselin, S.[Sébastien], Zhang, S.T.[Shao-Ting],
UM-CAM: Uncertainty-weighted multi-resolution class activation maps for weakly-supervised segmentation,
PR(160), 2025, pp. 111204.
Elsevier DOI Code:
WWW Link. 2501
Segmentation, Brain tumor, Class activation map, Exponential geodesic distance, Noisy label BibRef

Rehman, A.[Abbas], Naijie, G.[Gu], Aldrees, A.[Asma], Umer, M.[Muhammad], Hakeem, A.[Abeer], Alsubai, S.[Shtwai], Cascone, L.[Lucia],
Advancing brain tumor segmentation and grading through integration of FusionNet and IBCO-based ALCResNet,
IVC(154), 2025, pp. 105432.
Elsevier DOI 2502
Automated diagnosis, Brain tumor detection, Brain tumor grading, Brain tumor segmentation, Treatment planning BibRef

Ramamoorthy, H.[Hariharan], Ramasundaram, M.[Mohan], Raja, S.P., Randive, K.[Krunal],
An Efficient Classification of Multiclass Brain Tumor Image Using Hybrid Artificial Intelligence with Honey Bee Optimization and Probabilistic U-RSNet,
IJIG(25), No. 1, Januaury 2025, pp. 2450059.
DOI Link 2502
BibRef

Yu, L.[Luyue], Liu, C.Y.[Cheng-Yuan], Qu, A.[Aixi], Wu, Q.[Qiang], Liu, J.[Ju],
Multi-Modal Hybrid Encoding Approach Based on Information Bottleneck for Brain Tumor Grading,
SPLetters(32), 2025, pp. 651-655.
IEEE DOI 2502
Feature extraction, Genetics, Image coding, Image reconstruction, Training, Encoding, Cancer, Attention mechanisms, Fuses, multi-modal fusion BibRef

Zhou, L.F.[Li-Fang], Wang, Y.[Ya],
Brain tumor image segmentation based on shuffle transformer-dynamic convolution and inception dilated convolution,
CVIU(254), 2025, pp. 104324.
Elsevier DOI 2503
Brain tumor segmentation, Shuffle transformer, Dynamic convolution, Dilated convolution, Multi-scale BibRef

Weidner, J.[Jonas], Ezhov, I.[Ivan], Balcerak, M.[Michal], Metz, M.C.[Marie-Christin], Litvinov, S.[Sergey], Kaltenbach, S.[Sebastian], Feiner, L.[Leonhard], Lux, L.[Laurin], Kofler, F.[Florian], Lipkova, J.[Jana], Latz, J.[Jonas], Rueckert, D.[Daniel], Menze, B.[Bjoern], Wiestler, B.[Benedikt],
A Learnable Prior Improves Inverse Tumor Growth Modeling,
MedImg(44), No. 3, March 2025, pp. 1297-1307.
IEEE DOI 2503
Tumors, Brain modeling, Computational modeling, Predictive models, Mathematical models, Training, Radiation therapy, Robustness, inverse biophysics BibRef

Kumar, D.[Dinesh], Sethi, D.[Dimple], Kussa, W.T.[Wagaye Tadele], Dana, Y.M.[Yeabsira Mengistu], Kag, H.[Hitesh],
3D U-Net-Based Brain Tumor Semantic Segmentation Using a Modified Data Generator,
IJIST(35), No. 2, 2025, pp. e70056.
DOI Link 2504
3D U-net, data augmentation, deep learning, modified data generator, segmentation of brain tumor BibRef

Chhabra, S.[Sumit], Bansal, K.[Khushboo],
An Efficient Brain Tumor Prediction Using Pteropus Unicinctus Optimization on Deep Neural Network,
IJIG(25), No. 3, May 2025, pp. 2550023.
DOI Link 2505
BibRef

Siddiqah, M.[Mariyam], Javed, K.[Kashif], Gilani, S.O.[Syed Omer], Khan, M.A.[Muhammad Attique], Alsenan, S.[Shrooq], Damaševicius, R.[Robertas], Zhang, Y.D.[Yu-Dong],
DSA: Deep Self-Attention Medical Transformer Neuro-Technology for Brain Tumor Segmentation,
IJIST(35), No. 3, 2025, pp. e70109.
DOI Link 2506
brain tumor segmentation (BraTS), enhancing tumor region, medical image segmentation, transfer learning BibRef

Zhang, Y.[Ye], Zhang, M.[Muqing], Zhang, J.X.[Jian-Xin], Shen, Y.Y.[Yang-Yang], Niu, D.[Datian],
GTMamba: Graph Tri-Orientated Mamba Network for 3D Brain Tumor Segmentation,
IJIST(35), No. 3, 2025, pp. e70111.
DOI Link 2506
brain tumor segmentation, Graph-Tom, Mamba, U-Net BibRef

Wu, X.S.[Xiao-Sheng], Hou, Q.Y.[Qing-Yi], Tang, C.S.[Chao-Sheng], Wang, S.H.[Shui-Hua], Sun, J.[Junding], Zhang, Y.D.[Yu-Dong],
Diff-CFFBNet: Diffusion-Embedded Cross-Layer Feature Fusion Bridge Network for Brain Tumor Segmentation,
IJIST(35), No. 3, 2025, pp. e70088.
DOI Link 2506
brain tumor, cross-layer feature fusion, deep learning, diffusion models, medical image segmentation BibRef

Zhu, K.[Kaiyan], Cao, W.[Weiye], Xu, J.H.[Jian-Hao], Liu, T.[Tong], Liu, Y.[Yue], Song, W.B.[Wei-Bo],
Modal Feature Supplementation Enhances Brain Tumor Segmentation,
IJIST(35), No. 3, 2025, pp. e70079.
DOI Link 2506
auxiliary network, brain tumors, feature enhancement, modal characteristics, multimodal medical imaging BibRef

Fan, Y.B.[Yan-Bing], Liu, L.H.[Ling-Hui], Luan, X.[Xiao], Li, W.S.[Wei-Sheng],
Reversible Feature Learning for Brain Tumor Segmentation With Incomplete Modalities,
SPLetters(32), 2025, pp. 2419-2423.
IEEE DOI 2507
Image segmentation, Brain tumors, Brain modeling, Transformers, Representation learning, Motion segmentation, Feature extraction, incomplete multi-modality BibRef

Guo, Q.[Qianren], Wang, Y.H.[Yue-Hang], Zhang, Y.J.[Yong-Ji], Qi, H.[Hong], Hu, Y.H.[Yu-Hua], Jiang, Y.[Yu],
Hyper-BTS: Brain tumor segmentation based on hypergraph guidance,
PR(169), 2026, pp. 111926.
Elsevier DOI 2509
Brain tumor segmentation, Hypergraph neural networks, Feature fusion, Disentangled representation learning BibRef

Lai, Y.[Yinyi], Cao, A.[Anbo], Gao, Y.[Yuan], Shang, J.Q.[Jia-Qi], Li, Z.Y.[Zong-Yu],
Advancing Efficient Brain Tumor Multi-Class Classification: New Insights From the Vision Mamba Model in Transfer Learning,
IJIST(35), No. 5, 2025, pp. e70177.
DOI Link 2509
brain tumor, medical imaging, multi-class classification, pre-trained models, transfer learning, vision mamba BibRef

Sun, Y.H.[Yong-Heng], Liu, M.X.[Ming-Xia], Lian, C.F.[Chun-Feng],
MGAEPL: Multi-Granularity Automated and Editable Prompt Learning for brain tumor segmentation,
PR(172), 2026, pp. 112509.
Elsevier DOI 2512
Prompt learning, Multi-task learning, Medical image segmentation, Prediction BibRef

Preložnik, D.[Domen], Špiclin, Ž.[Žiga],
Cross-modality white matter lesion segmentation by modality de-indentification,
PRL(199), 2026, pp. 120-127.
Elsevier DOI 2512
Cross-modality, Lesion segmentation, Modality classification, Unsupervised domain adaptation BibRef

Madni, H.A.[Hussain Ahmad], Shujat, H.[Hafsa], de Nardin, A.[Axel], Zottin, S.[Silvia], Foresti, G.L.[Gian Luca],
FsBAD: Data-efficient feature reconstruction for few-shot brain anomaly detection,
PRL(199), 2026, pp. 113-119.
Elsevier DOI 2512
Distribution regularization, Feature reconstruction, Few-shot learning, Medical anomaly detection BibRef

Alshurbaji, M.[Mohammad], Assefa, M.[Maregu], Obeid, A.[Ahmad], Seghier, M.L.[Mohamed L.], Hassan, T.[Taimur], Taha, K.[Kamal], Werghi, N.[Naoufel],
TriGAN-SiaMT: A triple-segmentor adversarial network with bounding box priors for semi-supervised brain lesion segmentation,
PRL(200), 2026, pp. 37-43.
Elsevier DOI 2601
Brain lesion segmentation, Deep learning, Semi-supervised learning, Siamese, Mean-Teacher BibRef

Yu, F.[Feng], Cao, J.C.[Jia-Cheng], Liu, L.[Li], Jiang, M.H.[Ming-Hua],
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor Segmentation,
CVPR25(5197-5206)
IEEE DOI Code:
WWW Link. 2508
Image segmentation, Accuracy, Convolution, Memory management, Brain tumors, Performance gain, Transformers, Decoding, learnable weight skip BibRef

Konwer, A.[Aishik], Hu, X.L.[Xiao-Ling], Bae, J.[Joseph], Xu, X.[Xuan], Chen, C.[Chao], Prasanna, P.[Prateek],
Enhancing Modality-Agnostic Representations via Meta-learning for Brain Tumor Segmentation,
ICCV23(21358-21368)
IEEE DOI 2401
BibRef