Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis.

Ahmmed, Syed; Podder, Prajoy; Mondal, M. Rubaiyat Hossain; Rahman, S.M. Atikur; Kannan, Somasundar; Hasan, Md; Rohan, Ali; Prosvirin, Alexander

Abstract

This study focuses on leveraging data-driven techniques to diagnose brain tumors through magnetic resonance imaging (MRI) images. Utilizing the rule of deep learning (DL), we introduce and fine-tune two robust frameworks, ResNet 50 and Inception V3, specifically designed for the classification of brain MRI images. Building upon the previous success of ResNet 50 and Inception V3 in classifying other medical imaging datasets, our investigation encompasses datasets with distinct characteristics, including one with four classes and another with two. The primary contribution of our research lies in the meticulous curation of these paired datasets. We have also integrated essential techniques, including Early Stopping and ReduceLROnPlateau, to refine the model through hyperparameter optimization. This involved adding extra layers, experimenting with various loss functions and learning rates, and incorporating dropout layers and regularization to ensure model convergence in predictions. Furthermore, strategic enhancements, such as customized pooling and regularization layers, have significantly elevated the accuracy of our models, resulting in remarkable classification accuracy. Notably, the pairing of ResNet 50 with the Nadam optimizer yields extraordinary accuracy rates, reaching 99.34% for gliomas, 93.52% for meningiomas, 98.68% for non-tumorous images, and 97.70% for pituitary tumors. These results underscore the transformative potential of our custom-made approach, achieving an aggregate testing accuracy of 97.68% for these four distinct classes. In a two-class dataset, Resnet 50 with the Adam optimizer excels, demonstrating better precision, recall, F1 score, and an overall accuracy of 99.84%. Moreover, it attains perfect per-class accuracy of 99.62% for 'Tumor Positive' and 100% for 'Tumor Negative', underscoring a remarkable advancement in the realm of brain tumor categorization. This research underscores the innovative possibilities of DL models and our specialized optimization methods in the domain of diagnosing brain cancer from MRI images.

Citation

AHMMED, S., PODDER, P., MONDAL, M.R.H., RAHMAN, S.M.A., KANNAN, S., HASAN, M.J., ROHAN, A. and PROSVIRIN, A.E. 2023. Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis. Biomedinformatics [online], 3(4), pages 1124-1144. Available from: https://doi.org/10.3390/biomedinformatics3040068

• Journal Article Type: Article
• Acceptance Date: Nov 23, 2023
• Online Publication Date: Dec 6, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Jan 12, 2024
• Publicly Available Date: Jan 12, 2024
• Journal: BioMedInformatics
• Print ISSN: 2673-7426
• Electronic ISSN: 2673-7426
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 4
• Pages: 1124-1144
• DOI: https://doi.org/10.3390/biomedinformatics3040068
• Keywords: Brain tumor; MRI; Transfer learning; Inception net; ResNet 50; Convolution layer
• Public URL: https://rgu-repository.worktribe.com/output/2166806