Medical image processing by computers has become an area of growing importance, with researchers shifting their attention to various groundbreaking state-of-the-art models that aim to ease the pressure on doctors. Notwithstanding, image segmentation and feature extraction are critical yet challenging tasks in clinical diagnostics, hindered by issues such as noise and artifacts, limited annotated data, and the need for high computational efficiency. This research focuses on developing an EfficientNet-U-Net model, which aims to improve medical image segmentation for detecting skin lesions utilizing the International Skin Imaging Collaboration Challenge (ISIC) 2018 dataset. The model combines t... More >