A Browning and vegetation index-based deep neural network for melanoma skin cancer classification

Cutaneous melanoma accounts for most skin cancer deaths due to its high metastatic potential, making early detection essential. This study investigates whether color indices can enhance dermatoscopic images and improve benign–malignant classification. Three Browning Indices (Aimonino, Fetuga, Lunadei2) and one Vegetation Index (VI) were combined to form composite chromatic representations of RGB images. A Power-Log Based Histogram Equalization technique was applied for contrast enhancement. Four CNN architectures (VGG19, ResNet50, NASNetMobile, EfficientNetV2B0) were trained on the Javid Melanoma Dataset (10,605 images). Performance was assessed using AUC, F1-score, ROC/PR curves, ablation studies, and statistical significance tests. Chromatic-index preprocessing improved performance across all models. NASNetMobile and EfficientNetV2B0 achieved the best results, with AUCs of 96.20% and 95.50% and F1-scores of 90.10% and 89.50%. Ablation studies confirmed consistent gains over RGB-only inputs. The proposed BI/VI-based preprocessing enhances lesion saliency and improves melanoma classification accuracy without requiring segmentation, offering an interpretable strategy for early detection.