Adaptive Swarm-Driven Skin Lesion Segmentation and Classification Using Boundary-Aware U-Net and Hybrid Vision Transformer

Thilagavathi Rajamanickam (Department of Mathematics, Rajalakshmi Engineering College, Chennai, India)
Anuradha Konidena (Department of CSE, Koneru Lakshmiah Education Foundation (KLEF), (Deemed to be University), Vaddeswaram, Guntur, Andra Pradesh, India)
Mahesh N (Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai Erode, India)
Balakrishnan M (Department of Artificial Intelligence and Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu)
N.K. Priyadharsini (Department of Computer Science Engineering, P. A. College of Engineering and Technology, Pollachi, Coimbatore, India)
Suganthi Rajamanickam (Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India.)

Journal of Electronics, Electromedical Engineering, and Medical Informatics
Vol 8 No 4 (2026): October

Skin cancer is one of the most prevalent dermatological diseases worldwide, and timely and accurate diagnosis is essential for effective treatment and improved patient outcomes. However, automated skin lesion analysis remains challenging due to irregular lesion boundaries, low contrast between lesions and surrounding skin, imaging artifacts, variations in lesion appearance, and significant intra-class variability. To address these challenges, this research proposes an Adaptive Swarm-Driven Skin Lesion Segmentation and Classification Approach that integrates an Adaptive Boundary-Aware U-Net (ABAU-Net), a Butterfly Optimization Algorithm–Elephant Herding Optimization (BOA-EHO) strategy, and a Hybrid Vision Transformer with Dynamic Feature Fusion (HViT-DFF). The proposed ABAU-Net is designed to enhance segmentation accuracy by emphasizing boundary information and preserving fine-grained lesion structures, particularly in cases involving poorly defined or irregular borders. The BOA-EHO optimization strategy combines global exploration and local exploitation mechanisms to improve network parameter optimization, accelerate convergence, and reduce the possibility of premature convergence. Following segmentation, the HViT-DFF model integrates CNN-based local spatial features with transformer-based global contextual representations through dynamic feature fusion, enabling robust discrimination among multiple skin lesion classes. Extensive experiments conducted on the ISIC skin lesion dataset demonstrate the effectiveness of the proposed framework, achieving a Dice coefficient of 98.2%, Jaccard index of 97.5%, classification accuracy of 98.8%, and AUC of 0.992. These results outperform recent CNN-, Transformer-, and hybrid-based approaches. Overall, the proposed framework demonstrates strong potential for accurate, reliable, and automated skin lesion segmentation and multi-class classification, supporting computer-aided dermatological diagnosis and potentially facilitating earlier clinical intervention.