An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation

• URL: http://arxiv.org/abs/2506.09161v1
• Date: Tue, 10 Jun 2025 18:19:56 GMT
• Title: An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation
• Authors: Rajan Das Gupta, Md Imrul Hasan Showmick, Mushfiqur Rahman Abir, Shanjida Akter, Md. Yeasin Rahat, Md. Jakir Hossen,
• Abstract summary: We present a deep learning-based system capable of identifying both brain tumors and strokes from MRI images, along with their respective stages.<n>We have executed two groundbreaking strategies involving convolutional neural networks, MobileNet V2 and ResNet-50.<n>The models achieved strong performance, with training accuracy reaching 93% and validation accuracy up to 88%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early and accurate detection of brain abnormalities, such as tumors and strokes, is essential for timely intervention and improved patient outcomes. In this study, we present a deep learning-based system capable of identifying both brain tumors and strokes from MRI images, along with their respective stages. We have executed two groundbreaking strategies involving convolutional neural networks, MobileNet V2 and ResNet-50-optimized through transfer learning to classify MRI scans into five diagnostic categories. Our dataset, aggregated and augmented from various publicly available MRI sources, was carefully curated to ensure class balance and image diversity. To enhance model generalization and prevent overfitting, we applied dropout layers and extensive data augmentation. The models achieved strong performance, with training accuracy reaching 93\% and validation accuracy up to 88\%. While ResNet-50 demonstrated slightly better results, Mobile Net V2 remains a promising option for real-time diagnosis in low resource settings due to its lightweight architecture. This research offers a practical AI-driven solution for early brain abnormality detection, with potential for clinical deployment and future enhancement through larger datasets and multi modal inputs.

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