Abstract
Automated brain disorder classification for convenient treatment is one of the most complicated and widely spread problems. With the help of cutting-edge hardware, deep learning approaches are outperforming conventional brain disorder classification techniques in the medical image field. To solve this problem researchers have developed various transfer learning-based techniques. Pre-trained deep learning architectures are used here for feature extraction. This paper proposes a deep learning framework that includes a pre-trained fine-tuned EfficientNet B1 model to classify three different types of brain disorder and a normal category with \(93\%\) of test accuracy. In order to evaluate the proposed framework, the dataset was trained and validated using additional deep learning models Inception V3 and ResNet50 V2 for feature extraction using softmax and support vector machine (SVM) classifiers and employing three primary optimizers: stochastic gradient descent (SGD), root mean squared propagation (RMSProp), and Adam. The EfficientNet B1 with softmax classifier and Adam optimizer outperformed the other two state-of-the-art models and achieved the best results.
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References
Alhassan AM, Zainon WMNW (2021) Brain tumor classification in magnetic resonance image using hard swish-based relu activation function-convolutional neural network. Neural Comput Appl 33(15):9075–9087
Alyami J, Rehman A, Almutairi F, Fayyaz AM, Roy S, Saba T, Alkhurim A (2023) Tumor localization and classification from MRI of brain using deep convolution neural network and salp swarm algorithm. Cognit Comput 1–11
Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 39(1):63–74
Aurna NF, Yousuf MA, Taher KA, Azad A, Moni MA (2022) A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models. Comput Biol Med 146:105539
Brima Y, Tushar MHK, Kabir U, Islam T (2021) Deep transfer learning for brain magnetic resonance image multi-class classification. arXiv preprint arXiv:2106.07333
Cancernet, brain tumor: statistics. https://www.cancer.net/cancer-types/brain-tumor/statistics.”
Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10):e0140381
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Deepak S, Ameer P (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345
Deepak S, Ameer P (2021) Automated categorization of brain tumor from MRI using CNN features and SVM. J Ambient Intell Humaniz Comput 12(8):8357–8369
Deepa S, Janet J, Sumathi S, Ananth J (2023) Hybrid optimization algorithm enabled deep learning approach brain tumor segmentation and classification using MRI. J Digital Imaging 36:1–22
Ghosh A, Soni B, Baruah U, Murugan R (2022) Classification of brain hemorrhage using fine-tuned transfer learning. Advanced machine intelligence and signal processing. Springer, Berlin, pp 519–533
Haq EU, Jianjun H, Li K, Haq HU, Zhang T (2021) An MRI-based deep learning approach for efficient classification of brain tumors. J Ambient Intell Humaniz Comput 14:1–22
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D (2018) Deep networks in identifying CT brain hemorrhage. J Intell Fuzzy Syst 35(2):2215–2228
Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh RS (2017) Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Appl Sci 8(1):27
Kibriya H, Masood M, Nawaz M, Nazir T (2022) Multiclass classification of brain tumors using a novel CNN architecture. Multimed Tools Appl 81:1–17
Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400
Mehrotra R, Ansari M, Agrawal R, Anand R (2020) A transfer learning approach for AI-based classification of brain tumors. Machine Learn Appl 2:100003
Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378
O’Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458
Polat Ö, Güngen C (2021) Classification of brain tumors from MR images using deep transfer learning. J Supercomput 77(7):7236–7252
Poyraz AK, Dogan S, Akbal E, Tuncer T (2022) Automated brain disease classification using exemplar deep features. Biomed Signal Process Control 73:103448
Rane C, Mehrotra R, Bhattacharyya S, Sharma M, Bhattacharya M (2021) A novel attention fusion network-based framework to ensemble the predictions of CNNS for lymph node metastasis detection. J Supercomput 77(4):4201–4220
Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747
Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182
Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, PMLR, pp. 6105–6114
Tasci B (2023) Automated ischemic acute infarction detection using pre-trained CNN models’ deep features. Biomed Signal Process Control 82:104603
TUNCER T (2023) kaggle datasets download -d turkertuncer/brain-disorders-four-categories. Accessed
Usmani IA, Qadri MT, Zia R, Alrayes FS, Saidani O, Dashtipour K (2023) Interactive effect of learning rate and batch size to implement transfer learning for brain tumor classification. Electronics 12(4):964
Veni N, Manjula J (2022) High-performance visual geometric group deep learning architectures for MRI brain tumor classification. J Supercomput 78:1–12
Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big data 3(1):1–40
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Ghosh, A., Soni, B. & Baruah, U. A Fine-Tuned EfficientNet B1 Based Deep Transfer Learning Framework for Multiple Types of Brain Disorder Classification. Iran J Sci Technol Trans Electr Eng (2024). https://doi.org/10.1007/s40998-024-00726-w
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DOI: https://doi.org/10.1007/s40998-024-00726-w