Deep Learning -Based Automated Skin Lesion Detection And Classification

Abstract

Skin cancer is a prevalent and potentially life-threatening condition, emphasiz- ing the need for accurate and timely diagnosis. Leveraging advancements in deep learning, particularly convolutional neural networks (CNNs), this study presents an automated approach for the diagnosis of skin lesions to aid in early detection. A diverse dataset comprising various skin lesion types, encompassing different skin tones and image qualities, is collected and preprocessed. Transfer learning from pre-trained models is employed to leverage feature representations learned on large- scale datasets. The model is fine-tuned on the skin lesion dataset, and hyperparame- ter tuning is performed to optimize performance. Validation and testing on separate datasets confirm the model’s generalization capability. Post-processing techniques and interpretability measures enhance the reliability of model predictions. The de- veloped system demonstrates promising results, providing a valuable tool for der- matologists in clinical settings. The study emphasizes the importance of continuous collaboration with medical professionals, ethical considerations, and adherence to regulatory standards in the deployment of deep learning-based diagnostic tools in healthcare.