Use of Deep Learning for Automated Skin Lesion Detection and its Impact on Healthcare

Abstract

Skin cancer, including malignant melanoma and non-melanoma variants, continues to represent a significant public health concern worldwide. Early and accurate detection of skin lesions is critical for effective treatment and improved patient outcomes. This manuscript explores the application of deep learning techniques—particularly convolutional neural networks (CNNs)—for automated skin lesion detection. By leveraging publicly available datasets, such as those from the International Skin Imaging Collaboration (ISIC), this study evaluates the performance of deep learning models in classifying and segmenting skin lesions. Our findings demonstrate that deep learning models can achieve high accuracy, sensitivity, and specificity in skin lesion detection, suggesting a promising tool for early diagnosis in clinical settings. We discuss the methodology, key experiments, and outcomes, and we highlight the potential benefits and challenges associated with integrating these automated systems into the healthcare infrastructure.