Segmenting Skin Lesions in Medical Imaging A Transfer Learning Approach

The field of medical image analysis has been radically transformed by deep learning techniques, particularly Convolutional Neural Networks (CNNs), which have enabled high-accuracy diagnoses, even in the presence of insufficient data. Recent developments have facilitated the development of automated systems that are capable of diagnosing diseases with minimal human intervention. The increasing prevalence of skin cancer poses a substantial challenge because of the difficulty in identifying lesions, which are frequently distinguish by irregular boundaries, noise artifacts, and low contrast, among other health implications. This necessitates the laborious and complex process of image segmentation, which involves the separation of the lesion from the adjacent tissue, in the development of precise diagnostic systems. To address these obstacles, this study implements a hair removal step in conjunction with a segmentation model that substitutes the conventional UNet encoder with a pre-trained Xception architecture. Later, the model is fine-tuned to improve the performance of skin lesion segmentation by applying the power of transfer learning. To assess its generalizability, our model is tested on the PH2 dataset and trained on the ISIC 2018 dataset. The proposed model’s effectiveness in accurately segmenting skin lesions is demonstrated by the experimental results, which indicate that it successfully obtains a segmentation accuracy of 96.221%, an IoU of 90.406%, and a Dice Coefficient of 94.418% on the ISIC 2018 dataset.