Robustness of a neural network approved for skin cancer diagnosis against variations in sequential images.

Publication date: Jul 06, 2026

Convolutional neural networks (CNN) for skin cancer classification have shown results comparable to dermatologists but are vulnerable to minor image transformations. We investigated the robustness of a MDR class-IIa certified CNN when classifying sequential images of identical lesions. We acquired 2,744 dermoscopic images of 385 skin lesions (80. 8 % benign, 19. 2 % malignant) and applied in-vivo zoom, rotation (90-degree increments), and simple repetitions of image recordings. Sequential images of identical lesions were classified by a binary CNN (Moleanalyzer-Pro, FotoFinder Systems, Germany) and the variability of scores was investigated using intraclass correlation coefficient (ICC), mean absolute change of scores (mac), and probability of change of predicted class ( ). In dermoscopic baseline images (n = 385) the CNN showed a sensitivity, specificity, and area under the receiver operating characteristic (AUROC) (95 % CI) of 91. 9 % (83. 4 %-96. 2 %), 87. 8 % (83. 7 %-91. 0 %) and 0. 947 (0. 921-0. 972), respectively. The ICC across images of identical lesions was 0. 872 (0. 862-0. 883), indicating excellent reliability. Overall mac of scores was 0. 102 (0. 090-0. 115) and was 7. 5 % (5. 8 %-9. 2 %). The tested CNN demonstrated a profound robustness against image variations as might be introduced during sequential digital dermoscopy. Clinically relevant class changes occurred in one of 13 images.

Semantics

Original Article