Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50

• Published in: BMC medical imaging Vol. 24; no. 1; p. 107
• Main Authors: M, Mohamed Musthafa, T R, Mahesh, V, Vinoth Kumar, Guluwadi, Suresh
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.05.2024; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain research; Brain tumor detection; Brain tumors; Computer-aided manufacturing; Data augmentation; Datasets; Decision making; Deep Learning; Efficiency; Explainable AI; Explainable artificial intelligence; Grad-CAM; Humans; Image analysis; Image enhancement; Image Interpretation, Computer-Assisted - methods; Image processing; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medical imaging equipment; MRI images; Neural networks; ResNet50; Tumors; Deep learning; Explainable AI; ResNet50; Grad-CAM; Medical image analysis; MRI images; Brain tumor detection
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-024-01292-7

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.