AI in Image Analysis and Interpretation: Enhancing Diagnostic Precision

Abstract

The rapid advancement in artificial intelligence (AI) has revolutionized medical image analysis, offering substantial improvements in diagnostic accuracy and interpretability. This paper introduces a complete AI-based system that improves the accuracy of brain MRI interpretation diagnosis through a mixture of deep learning models and explainable AI solutions. Its methodology consists of preprocessing, expert-based annotation, hybrid feature extractions, and design of model specific architecture. It used denseNet to classify, Mask R-CNN to segment, and the retinanet to detect tumors which yielded strong results in tasks. The proposed classification model gave a highest accuracy of 98.1 on the Kaggle brain MRI dataset, which was better than the traditional CNN (94.3%) and SVM (90.7%) models. The Dice coefficient (96.9% and IoU at 93.7) and tumor boundary localization in segmentation using Mask R-CNN were found to be high with high accuracy of the localization. The RetinaNet detection model had an mAP@0.5 of 94.4% and had very low false positives. SHAP and Grad-CAM ensured interpretability, and the trust rating of clinicians was more than 4.8/5. These findings confirm the clinical relevance, transparency, and diagnostic strength of the model. The pipeline of this study combines explainability and performance as it helps in closing the gap between AI predictions and clinical decision-making. Not only does this piece enhance detection and segmentation accuracy, but also helps develop trust by providing explanations (visually). The given system will have implication to the integration into real time diagnostic systems and the ability to convert it to other imaging modalities. Future directions involve the adaptation of domains to multi-center operation, minimization of computing intensity to run edge-based inference, and generalization to multi-class tumor classification to predict a wider range of neurological diagnoses.

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