Integrating Advanced Machine Learning Techniques for Multi-Class Skin Disease Diagnosis

Dermatology is the study of skin disorders that can differ based on location and environmental conditions. Skin is challenging to analyze due to its unevenness and other factors, and those in remote areas may lack access to medical care and neglect symptoms, which can worsen over time. Thus, an automated system for identifying skin diseases is necessary. Our deep learning model distinguishes between healthy and diseased skin and categorizes diseases such as Melanocytic Nevi, ActinicKeratoses, Melanoma, Dermatofibroma,Benign keratosis-like lesions, Basal cell Carcinoma and Vascular lesions. We employed a form of machine learning known as Deep Learning that uses large datasets to decrease the number of classifiers needed. Our model uses the Convolutional Neural Network (CNN) algorithm to classify images. Skin conditions can be infectious and life-threatening if not detected early. Most are caused by overexposure to Ultraviolet Radiation (UR). Our system aims to minimize risks by detecting skin diseases automatically. However, skin appearance can vary, which poses a challenging task.