Leakage-Controlled Evaluation of Handcrafted-Deep Feature Fusion and Confidence Stacking for Four-Class Skin Lesion Classification

Abstract

Skin lesion diagnosis remains one of the most challenging tasks in medical image analysis because several benign and malignant lesions exhibit overlapping visual characteristics such as irregular borders, non-uniform pigmentation, structural asymmetry, and texture similarity. Although deep learning methods have achieved remarkable progress in dermoscopic image classification, many existing systems depend exclusively on convolutional neural networks and may not fully benefit from clinically interpretable handcrafted descriptors or classifier confidence information. In addition, direct feature fusion strategies often fail to maximize the complementary relationship between handcrafted and deep representations. This study proposes a Sequential Hybrid Meta-Learning Model for automated four-class skin lesion classification involving basal cell carcinoma, melanoma, nevus, and pigmented benign keratosis. The proposed framework integrates handcrafted dermatological image descriptors, pretrained deep convolutional neural network embeddings, and decision-level classifier confidence scores within a multi-stage learning pipeline. A confidence-margin data cleaning stage is introduced to reduce noisy or ambiguous samples, followed by dataset balancing and hierarchical cancer/non-cancer diagnostic decisioning. Unlike conventional direct-fusion approaches, the proposed model learns sequentially by first extracting deep discriminative knowledge and then reusing classifier confidence outputs as meta-features for final decision making. Experimental evaluation demonstrates substantial performance gains over baseline hybrid systems. The model achieved 95.87% flat four-class accuracy, 94.11% hierarchical accuracy, 95.64% cancer/non-cancer Level-1 accuracy, 0.992245 ROC-AUC, and 0.991202 PR-AUC. The results confirm that combining feature-level and decision-level knowledge significantly improves skin lesion discrimination and offers a practical computer-aided diagnostic solution for intelligent dermatology screening.