Classification des IRM cérébrales Pathologiques avec Optimisation

Abstract

The automatic classification of brain MRI images represents a fundamental challenge in the medical field, as it effectively assists practitioners in diagnosing cerebral pathologies. In this study, we explored several artificial intelligence approaches for multi-class brain tumor classification by evaluating different models : a traditional Convolutional Neural Network (CNN), the ResNet-18 model with and without optimization, the same model combined with the symbolic DRB classifier, and the Vision Transformer (ViT) model in both its base and optimized versions. Model optimization was performed using the Adam algorithm, known for its rapid convergence. Experimental results demonstrated varying performance across the tested models. The optimized CNN achieved a notable accuracy of 98.02%.confirming its effectiveness despite its relative simplicity. The optimized ResNet 18 delivered excellent performance with 99.08% accuracy and F1-scores exceeding 0.98 for most classes, indicating strong generalization and optimal model tuning. In contrast, its DRB classifier-integrated version without optimization reached 91.61%, highlighting both the potential and limitations of this combination without prior refinement. The ViT model’s case was particularly remarkable : without optimization, it achieved only 25.30% accuracy, reflecting poor initial learning capability. However, after optimization, its performance surged to 99.79%, making it the best-performing model in the study. This result clearly demonstrates the crucial importance of optimization in machine lear ning systems, particularly for advanced architectures. In summary, this work highlights the complementary roles of model architecture, classifier selection, and optimization tech niques in developing high-performance intelligent systems for brain MRI classification. The findings open promising avenues for integrating these models into clinical diagnostic tools to enhance accuracy and efficiency in medical diagnosis