A novel hybrid deep learning pipeline for precise segmentation of skin cancer lesions.

Introduction: Skin cancer is among the most prevalent malignancies worldwide, and accurate segmentation of skin lesions plays a vital role in its early diagnosis and treatment. Traditional deep learning models face challenges in balancing local feature extraction with global contextual understanding. This study aims to develop a novel, robust, and efficient hybrid model that combines lightweight CNNs and ViTs to enhance the accuracy and reliability of automated skin lesion segmentation for use in diverse clinical settings. Methods: The proposed framework is divided into several stages. The first stage is data preparation, which ensures consistency across datasets by standardizing input images. The second stage employs a lightweight CNN module to extract important local features from the input images. These features are then passed to a ViT module to capture long-range dependencies and global context. Finally, a segmentation head processes the combined features to produce the final lesion mask. Three publicly available datasets (ISIC 2020, PH2, HAM10000) were used for training, testing, and generalization evaluation. Results: The proposed hybrid architecture achieved an accuracy of 95.89%, a Dice coefficient of 94.29%, and a Jaccard index of 96.91% using the SGD optimizer on the ISIC 2020 dataset, outperforming several baseline and state-of-the-art methods. Cross-dataset evaluation on PH2 and HAM10000 yielded accuracies of 94.61% and 95.68%, respectively. Discussion: The proposed hybrid model presents a promising solution for accurate and efficient skin lesion segmentation, contributing to enhanced clinical decision-making in dermatological diagnostics.