An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms - Burhanettin Ozdemir; Ishak Pacal (Results in Engineering)
The skin, the body's largest organ, plays a critical role in protection and regulation, making its health essential. Skin cancer, one of the most prevalent malignancies, continues to rise globally and presents significant risks when diagnosis is delayed. Accurate detection is challenging due to the subtle and overlapping features of skin lesions, often leading to diagnostic errors. Deep learning has emerged as a powerful tool, capable of analyzing complex dermatological data and improving diagnostic accuracy through precise pattern recognition. This study proposes a novel lightweight and mobile-friendly hybrid model that combines ConvNeXtV2 blocks and focal self-attention mechanisms, addressing challenges such as data imbalance and model complexity. The Proposed Model employs ConvNeXtV2 in the first two stages for superior local feature extraction, while focal self-attention in the subsequent stages enhances sensitivity by focusing on diagnostically relevant regions. The Proposed Model was evaluated on the ISIC 2019 dataset, encompassing eight skin cancer classes with significant class imbalances, such as the Melanocytic Nevus class having 51 times more images than the Vascular Lesion class. Despite these disparities, the Proposed Model achieved robust performance across all classes, with 93.60% accuracy, 91.69% precision, 90.05% recall, and a 90.73% F1-score. Compared to baseline models and existing literature, it demonstrated a 10.8% improvement in accuracy over ResNet50 and a 3.3% improvement over the best-performing vision transformer (Swinv2-Base). This innovative design establishes a new benchmark in skin cancer detection, offering accurate, scalable, and generalizable predictions to support early diagnosis and improved clinical outcomes.