Automated Disease Classification in Dermatology: Leveraging Deep Learning for Skin Disorder Recognition

Abstract

Automated disease classification in dermatology has seen remarkable advancements owing to the application of deep learning techniques. This study delves into the utilization of deep learning models for skin disorder recognition, revolutionizing the field of dermatological diagnosis. Leveraging convolutional neural networks (CNNs) and other deep learning architectures, this research explores their efficacy in accurately classifying various skin disorders. The study employs extensive datasets comprising dermatological images, clinical data, and histopathological information, facilitating the development and evaluation of robust models for automated disease classification. Key considerations encompass model training, validation, and optimization to achieve high accuracy and sensitivity in identifying diverse skin conditions. The findings underscore the potential of deep learning-based approaches in enhancing diagnostic precision and expediting dermatological assessments, thereby significantly impacting clinical workflows and patient care.