STLC-Net: Skin Transfer Learning Classification Framework for Multi-Class Skin Cancer Detection and Clinical Decision Support

Abstract

Skin cancer remains one of the most widespread and fatal malignancies globally, making early, accurate diagnosis essential for improving patient survival rates. While deep Convolutional Neural Networks (CNNs) have advanced automated dermoscopic image analysis, practical clinical adoption is hindered by severe dataset class imbalance, data leakage, and diagnostic opacity. To overcome these challenges, this study introduces STLC-Net (Skin Transfer Learning Classification Network), a robust, zero-data-leakage framework for automated seven-class skin lesion classification using the benchmark HAM10000 dataset. The methodology integrates in-graph dynamic data augmentation to eliminate framework shape-locking errors during deployment, and incorporates a log-smoothed class-weighting strategy to stabilize gradient descent against severe minority class scarcity. Under a strict zero-leakage evaluation protocol (2,003 isolated test samples), three distinct architectural paradigms—DenseNet121 (dense feature concatenation), EfficientNetB3 (compound scaling), and MobileNetV2 (inverted residuals)—were trained via a two-stage transfer learning protocol. DenseNet121 emerged as the superior standalone backbone, achieving an accuracy of 79.73%, precision of 83.02%, and an ROC-AUC of 0.9398. To further eliminate individual architectural blind spots, a soft-voting STLC-Ensemble was constructed, achieving peak performance with 80.38% accuracy, 83.53% weighted precision, 79.85% F1-score, and an ROC-AUC of 0.9492. The complete pipeline is integrated into an interactive web deployment for real-time, interpretable clinical decision support.