An end-to-end agentic framework compares CNNs and vision transformers across three medical imaging tasks and adds LLM-generated structured reports
This work presents an end-to-end comparative medical imaging framework that evaluates ResNet50, EfficientNet-B0, DenseNet121, DeiT-Small, and Swin-Tiny across three heterogeneous tasks—chest X-ray pneumonia classification, brain MRI tumor detection, and dermoscopic skin cancer classification—integrating transfer learning, class-imbalance handling, model calibration, bootstrap confidence intervals, robustness evaluation, failure-case analysis, Grad-CAM explainability, ensemble learning, and extensive performance metrics, together with an LLM-driven reporting component constrained to research-oriented assistance that generates structured model-comparison summaries, explainability interpretations, and decision-support reports; results indicate that CNNs remain highly effective for structured