The rapid development of machine learning (ML) and deep learning (DL) has greatly advanced Alzheimer's disease (AD) diagnosis. However, existing models struggle to capture weak structural features in the marginal regions of brain MRI images, leading to limited diagnostic accuracy. To address this challenge, we introduce a Dual-Branch Convolutional Neural Network (DBCNN) equipped with a Learnable Edge Detection Module designed to jointly learn global semantic representations and fine-grained edge cues within a unified framework. Experimental results on two public datasets demonstrate that DBCNN significantly improves classification accuracy, surpassing 98%. Notably, on the OASIS dataset, it achieved an average accuracy of 99.71%, demonstrating strong generalization and robustness. This high diagnostic performance indicates that the model can assist clinicians in the early detection of Alzheimer's disease, reduce subjectivity in manual image interpretation, and enhance diagnostic consistency. Overall, the proposed approach provides a promising pathway toward intelligent, interpretable, and computationally efficient solutions for MRI-based diagnosis, offering strong potential to support early clinical decision-making.
Content-based mammographic image retrieval requires exact BIRADS categorical matching across five classes, posing far greater complexity than conventional binary classification. Existing studies remain limited by small sample sizes, improper patient-level separation, and inadequate statistical validation, restricting clinical translation. We developed a comprehensive evaluation framework systematically comparing CNN architectures (DenseNet121, ResNet50, VGG16) under advanced training strategies: fine-tuning, metric learning, and super-ensemble optimization. Rigorous patient-stratified splits (1003 patients, two images each), 602 test queries, and bootstrap confidence intervals (1000 resamples) ensured reliable assessment. Advanced fine-tuning and test-time augmentation (TTA) yielded a precision@10 of 34.71% for DenseNet121 _AdvancedFT_TTA, a 25.74% improvement over the baseline ResNet50 (27.6%). Selective super-ensemble and metric learning approaches were further benchmarked under patient-exclusive splits, confirming robust performance across architectures. Statistical analysis (bootstrap CIs, n = 1000; t-tests p 0.8) validated significant gains and reproducibility. These results establish DenseNet121_AdvancedFT_TTA as the new state-of-the-art for five-class BIRADS retrieval while reducing computational cost.