Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network.

Objective: Skin diseases, caused by various pathogens including bacteria, viruses, and fungi, are prevalent globally and significantly affect patients' physical, emotional, and social well-being. Early and accurate detection of such conditions is critical to prevent progression, especially in cases of malignant skin lesions. This study aims to develop a dual-stage deep learning framework for the segmentation and classification of skin lesions, addressing challenges such as imbalanced data, lesion variability, and low contrast. Methods: We propose a two-phase framework: (i) Precise instance segmentation using U-Net with a Visual Geometry Group (VGG16 encoder) to isolate skin lesions and (ii) classification using EfficientFormer and SwiftFormer networks to evaluate performance on both balanced and imbalanced datasets. Experiments were conducted on three benchmark datasets: Human against machine with 10,000 training images (HAM10000), International Skin Imaging Collaboration (ISIC) 2018, and the newly released ISIC 2024 SLICE-3D dataset. For SLICE-3D, we evaluated both tabular-only and image + metadata fusion approaches using XGBoost classifier and ResNet-based classifier, respectively. Results: On the balanced HAM10000 dataset, EfficientFormerV2 achieved 97.11% accuracy, a 97.14% Conclusion: The proposed dual-stage deep learning framework demonstrates high accuracy and robustness across segmentation and classification tasks on diverse datasets. Its adaptability to large-scale, non-dermoscopic data such as SLICE-3D confirms its potential for deployment in real-world skin cancer triage and teledermatology applications.