BOUNDARY AWARE SALIENCY-BASED LEVEL SET WITH MOMENTUM CONTRAST METAFORMER FOR SKIN CANCER SEGMENTATION AND CLASSIFICATION

Abstract

Skin cancer is considered one of the most widespread and life-threatening cancers and remains a challenging task for dermatologists. This challenge arises due to the small boundaries and regions of affected tissue. Existing methods yield poor classification accuracydueto inefficient classifier performance in recognizingcomplexpatterns. Therefore, an effective skin cancer segmentation and classification method called Boundary-Aware Saliency-based Level Set (BASLS) with Momentum Contrast Metaformer (MC-Metaformer) is proposed in this research. BASLS enables improved lesion segmentation by identifying significant structural components along lesion edges. Using residual connections, MC-Metaformer provides a better gradient path, addressing gradient fading issues during deep feature extraction. Preprocessing is performed on the HAM10000, ISIC-2019, and ISIC-2020 datasets to improve and standardize the data. ResNet50 is used to extract relevant features for classification. Experimental results demonstrate that the proposed MC-Metaformer outperforms the Deep Convolutional Neural Network (DCNN), achieving classification accuracies of 99.58%, 99.32%, and 98.62% on the HAM10000, ISIC-2019, and ISIC-2020 datasets, respectively. These results confirm the robustness and efficiency of the model in accurate skin cancer segmentation and classification.