Real-Time Edge-to-Mobile Melanoma Screening Using Sensitivity-Optimized Deep Learning and TensorRT Acceleration

Abstract

This article presents a real-time edge-to-mobile melanoma image-analysis prototype combining lesion localization, sensitivity-oriented classification, ensemble fusion, and TensorRT-optimized embedded inference. Six classification architectures were evaluated across HAM10000, ISIC 2019, and ISIC 2020 using a unified training and sensitivity fine-tuning protocol. The selected ResNet-50 and MedFusionNet stack achieved AUC values of 0.985, 0.988, and 0.940, respectively, under the benchmark metadata conditions. At the frozen global threshold of θ=0.20, the corresponding sensitivities were 1.000, 0.981, and 0.948. These results are classification-stage metrics computed on full, uncropped test images; sharpness rejection and YOLO-based localization were evaluated separately. Replacing patient-specific metadata with the neutral deployment vector changed sensitivity by at most one melanoma case per benchmark dataset. On an NVIDIA Jetson Orin Nano, the complete pipeline with TensorRT FP16-optimized classifiers operated at more than 22 FPS with an end-to-end latency of 43.6 ms. The system has not been clinically validated, and the open-source implementation is provided to support reproducibility and further research.