Beyond Accuracy: Cross-Validated and Threshold-Optimized Deep Learning for Primary and Metastatic Melanoma Classification from Histopathological Patches - Raden Rara Kartika Kusuma Winahyu; Lathifah Alfat; Deyana Kusuma Wardani (Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya, 2026)
Accurate differentiation between primary and metastatic melanoma in histopathological assessment is critical for staging and therapeutic decision-making. Although deep learning models often report high classification accuracy, their robustness and threshold-dependent clinical behavior remain insufficiently examined. We propose a cross-validated and threshold-optimized deep learning framework for classifying 206 histopathological regions of interest (ROIs), partitioned in an 80:20 split into training (n = 164) and evaluation (n = 42) subsets, using a ResNet-18 backbone. On the hold-out evaluation set, the model achieved an AUC of 0.922. To evaluate generalization stability, stratified 5-fold cross-validation was conducted across all ROIs, yielding fold AUCs ranging from 0.904 to 0.973 and a mean AUC of 0.938 ± 0.024, with a pooled out-of-fold AUC of 0.916. At a decision threshold of 0.5, the model achieved 78.6% accuracy (macro F1 = 0.7846). Increasing the threshold to 0.8 improved accuracy to 85.7% (macro F1 = 0.856), accompanied by higher precision for metastatic melanoma (0.894) and improved recall for primary melanoma (0.904), underscoring clinically meaningful sensitivity–specificity trade-offs beyond AUC alone. Grad-CAM analysis demonstrated spatially coherent activation concentrated within tumor-dense regions in true positives, minimal activation in true negatives, and intermediate activation in a borderline false negative case (probability = 0.75), linking prediction confidence to morphologically relevant evidence. Collectively, these findings highlight the importance of cross-validation rigor, threshold calibration, and interpretability in advancing clinically reliable deep learning systems for melanoma classification.