Skin Disease Recognition Based on Deep Learning Algorithms: A Review

Abstract

The sharp increase in cases of melanoma and other skin cancers worldwide highlights the urgent need for improved diagnostic methods. Because skin lesions vary widely and access to dermatological knowledge is limited in resource-poor areas, traditional methods - which rely on visual inspection and clinical experience - have difficulty identifying diseases accurately. This situation requires innovative approaches to improve accessibility and diagnostic accuracy. To address these issues, this work uses deep learning (DL) and convolutional neural networks (CNNs). This paper is trying to transform skin cancer diagnosis through the use of large databases of dermoscopic images and advanced artificial intelligence algorithms. In order to evaluate the effectiveness of CNNs and DL in identifying skin diseases, we conducted a comprehensive analysis of the literature, focusing on the accuracy of skin cancer type classification. Our approach focused on model architectures, data preparation methods, and performance indicators while examining existing research using AI algorithms to diagnose skin cancer. With the ultimate goal of improving patient outcomes through early detection and accurate classification of skin conditions, this approach not only underscores the great potential of DL and CNN in transcending traditional diagnostic limitations, but also highlights the continued development of AI-based tools in pathology. Dermatology. Diagnosis.