Abstract
Brain tumors are one of the leading causes of death worldwide. Different types of brain tumors are known, so the choice of treatment depends directly on the type of tumor. The classification of brain tumors is very important as a complex and challenging problem in the field of image processing. Today, deep learning methods are used to classify brain tumors. In addition to being able to detect and automatically classify all types of brain tumors, these methods significantly reduce the diagnosis time and increase accuracy. In this paper, a deep learning-based model is proposed to classify brain tumors into three classes: glioma, meningioma, and pituitary tumor. In the first phase, the pre-trained network ResNet50 is used to extract features from MRI images. In the second phase, by proposing two attention mechanisms (depth-separable convolution-based channel attention mechanism and an innovative multi-head-attention mechanism), the most effective spatial and channel features are extracted and integrated. Finally, the classification phase is performed. Evaluations on the Figshare dataset showed an accuracy of 99.32%, which performs better than existing models. Therefore, the proposed model can accurately classify brain tumors and help neurologists and physicians make accurate diagnostic decisions.
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References
Abd El-Wahab BS, Nasr ME, Khamis S, Ashour AS (2023) BTC-fCNN: fast convolution neural network for multi-class brain tumor classification. Health Inf Sci Syst 11(1):3. https://doi.org/10.1007/s13755-022-00203-w
Ait Amou M, Xia K, Kamhi S, Mouhafid M (2022) A novel MRI diagnosis method for brain tumor classification based on CNN and Bayesian optimization. Healthcare (basel). https://doi.org/10.3390/healthcare10030494
Ayadi W, Elhamzi W, Charfi I, Atri M (2019) A hybrid feature extraction approach for brain MRI classification based on Bag-of-words. Biomed Signal Process Control 48:144–152. https://doi.org/10.1016/j.bspc.2018.10.010
Ayadi W, Charfi I, Elhamzi W, Atri M (2022) Brain tumor classification based on hybrid approach. Vis Comput 38(1):107–117. https://doi.org/10.1007/s00371-020-02005-1
Badža MM, Barjaktarović MČ (2020) Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6):1999
Cheng J (2017). Brain magnetic resonance imaging tumor dataset. Figshare MRI dataset version, 5
Demir F, Akbulut Y, Taşcı B, Demir K (2023) Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data. Biomed Signal Process Control 81:104424. https://doi.org/10.1016/j.bspc.2022.104424
Deng J, Dong W, Socher R, Li LJ, Kai L, Li F-F (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition
Gab Allah AM, Sarhan AM, Elshennawy NM (2021) Classification of brain MRI tumor images based on deep learning PGGAN augmentation. Diagnostics (basel). https://doi.org/10.3390/diagnostics11122343
Gull S, Akbar S, Naqi SM (2023) A deep learning approach for multi-stage classification of brain tumor through magnetic resonance images. Int J Imaging Syst Technol. https://doi.org/10.1002/ima.22897
Haq MA, Khan I, Ahmed A, Eldin SM, Alshehri A, Ghamry NA (2023) DCNNBT: a novel deep convolution neural network-based brain tumor classification modeL. Fractals 31(06):2340102. https://doi.org/10.1142/s0218348x23401023
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR)
Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR)
Khan MA, Khan A, Alhaisoni M, Alqahtani A, Alsubai S, Alharbi M, Malik NA, Damaševičius R (2023) Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm. Int J Imaging Syst Technol 33(2):572–587. https://doi.org/10.1002/ima.22831
Krishnapriya S, Karuna Y (2023) Pre-trained deep learning models for brain MRI image classification. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2023.1150120
Masood M, Nazir T, Nawaz M, Mehmood A, Rashid J, Kwon H-Y, Mahmood T, Hussain A (2021) A novel deep learning method for recognition and classification of brain tumors from MRI images. Diagnostics 11(5):744
Masoudi B (2023) VKCS: a pre-trained deep network with attention mechanism to diagnose acute lymphoblastic leukemia. Multimed Tools Appl 82(12):18967–18983. https://doi.org/10.1007/s11042-022-14212-0
Mehnatkesh H, Jalali SMJ, Khosravi A, Nahavandi S (2023) An intelligent driven deep residual learning framework for brain tumor classification using MRI images. Expert Syst Appl 213:119087. https://doi.org/10.1016/j.eswa.2022.119087
Minz A, Mahobiya C (2017) MR image classification using adaboost for brain tumor type. In: 2017 IEEE 7th international advance computing conference (IACC)
Muezzinoglu T, Baygin N, Tuncer I, Barua PD, Baygin M, Dogan S, Tuncer T, Palmer EE, Cheong KH, Acharya UR (2023) PatchResNet: multiple patch division-based deep feature fusion framework for brain tumor classification using MRI images. J Digit Imaging 36(3):973–987. https://doi.org/10.1007/s10278-023-00789-x
Nawaz SA, Khan DM, Qadri S (2022) Brain tumor classification based on hybrid optimized multi-features analysis using magnetic resonance imaging dataset. Appl Artif Intell 36(1):2031824. https://doi.org/10.1080/08839514.2022.2031824
Ramtekkar PK, Pandey A, Pawar MK (2023) Innovative brain tumor detection using optimized deep learning techniques. Int J Syst Assur Eng Manag 14(1):459–473. https://doi.org/10.1007/s13198-022-01819-7
Saha P, Das R, Das SK (2023) BCM-VEMT: classification of brain cancer from MRI images using deep learning and ensemble of machine learning techniques. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-15377-y
Saritha M, Paul Joseph K, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognit Lett 34(16):2151–2156. https://doi.org/10.1016/j.patrec.2013.08.017
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV)
Shahin AI, Aly S, Aly W (2023a) A novel multi-class brain tumor classification method based on unsupervised PCANet features. Neural Comput Appl 35(15):11043–11059. https://doi.org/10.1007/s00521-023-08281-x
Shahin AI, Aly W, Aly S (2023b) MBTFCN: a novel modular fully convolutional network for MRI brain tumor multi-classification. Expert Syst Appl 212:118776. https://doi.org/10.1016/j.eswa.2022.118776
Solanki S, Singh UP, Chouhan SS, Jain S (2023) Brain tumor detection and classification using intelligence techniques: an overview. IEEE Access 11:12870–12886. https://doi.org/10.1109/ACCESS.2023.3242666
Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Content-based brain tumor retrieval for mr images using transfer learning. IEEE Access 7:17809–17822. https://doi.org/10.1109/ACCESS.2019.2892455
Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. http://proceedings.mlr.press/v97/tan19a.html
Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT (2023) Transfer learning enables predictions in network biology. Nature 618(7965):616–624. https://doi.org/10.1038/s41586-023-06139-9
Tummala S, Kadry S, Bukhari SAC, Rauf HT (2022) Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling. Curr Oncol 29(10):7498–7511
Varuna Shree N, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Informat 5(1):23–30. https://doi.org/10.1007/s40708-017-0075-5
Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision—ECCV 2018. Cham
Zulfiqar F, IjazBajwa U, Mehmood Y (2023) Multi-class classification of brain tumor types from MR images using EfficientNets. Biomed Signal Process Control 84:104777. https://doi.org/10.1016/j.bspc.2023.104777
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Masoudi, B. An optimized dual attention-based network for brain tumor classification. Int J Syst Assur Eng Manag 15, 2868–2879 (2024). https://doi.org/10.1007/s13198-024-02300-3
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DOI: https://doi.org/10.1007/s13198-024-02300-3