A Comprehensive Study on Skin Disease Detection using Deep Learning Approaches

Abstract

Skin illnesses include a wide range of problems, from common dermatological issues to uncommon and complex disorders, and they collectively place a substantial strain on the world's healthcare systems. Immediate and accurate diagnosis is crucial for many disorders to be effectively controlled and treated, but it can be difficult because of the subjectivity of visual inspection and the variation in clinical presentations. The recent intersection of artificial intelligence and medicine has brought about innovative approaches to computer-aided diagnostics, resulting in changes in the field of dermatology. Because deep learning can analyze massive amounts of data and find complex patterns, it has become a powerful tool in the processing of identifying for more accurate and efficient diagnostic methods. The most recent advancements in deep learning techniques specifically created for the diagnosis of skin diseases are examined in this review paper. Examine the efficacy and performance of a number of algorithms, including the adaptable k-nearest neighbour, the robust support vector machine (SVM), and the complex convolutional neural networks (CNNs). Deep learning techniques for automated skin disease detection include generative adversarial networks (GANs) for creating synthetic data, recurrent neural networks (RNNs) for processing sequential data, and attention mechanisms for highlighting pertinent image regions. Every algorithm is carefully examined to determine its advantages and disadvantages, offering important information on how it might be used in dermatological practice. By shedding light on the field's emerging advancements in dermatology, this study aims to highlight a broader understanding of deep learning's potential to transform the diagnosis and treatment of skin disorders, ultimately improving patient outcomes and boosting the provision of healthcare services.