Skin Diagnostics Using Transfer Learning for Lesion Segmentation

Skin lesion segmentation is a critical component in skin diagnostics, as it aids both manual and computer-aided diagnostic processes by pinpointing specific areas of interest on the skin. Image segmentation, a fundamental task in computer vision, involves dividing a digital image into distinct segments. Deep neural networks have emerged as robust tools for this purpose. This research paper delves into the potential of deep learning methods for skin lesion segmentation. The study evaluates the performance of three prominent architectures: U-Net, SegNet, and DeepLabV3, assessing their efficacy in accurately delineating skin lesions and thereby advancing skin diagnostics. The proposed method undergoes extensive training and evaluation on the widely recognized ISIC skin lesion dataset. The methodological framework exhibits exceptional performance, positioning it as a promising candidate for developing a fully automated computer-aided skin lesion diagnostic system. Evaluation results highlight that DeepLabV3 outperforms U-Net and SegNet architectures in terms of accuracy, emphasizing its superiority for skin lesion segmentation tasks.