AI-Based Multiclass Classification of Skin Diseases in Dermoscopy Images Using Convolutional Neural Network

Skin contributes significantly to controlling body temperature, through sweating and dilation or spasm of blood vessels in response to external temperature changes. It also helps to protect against harmful microorganisms and toxins by producing antimicrobial peptides and other immune system components. Detecting skin dermatitis using an artificial intelligence-based Convolutional Neural Network (CNN) describes the development of a machine-learning model that can accurately classify skin diseases based on images. The study involved collecting a dataset of skin disease images and training a CNN model on this dataset. The model was able to achieve high accuracy in classifying various skin diseases such as eczema, psoriasis, and melanoma. The results demonstrate the potential of using CNNs as a diagnostic tool for dermatologists to accurately detect and diagnose skin diseases. The development of this model has the potential to improve patient outcomes and decrease the time and cost associated with traditional diagnosis methods.