Skin Disease Detection Using CNN (Convolutional Neural Network)

Abstract

Globally, skin diseases affect many people. Treatment and care for these diseases can only be effective if detected early and accurately. Machine learning and computer vision have advanced to the point that convolutional neural networks (CNNs) are now powerful tools for automatically detecting skin diseases. A study on the application of CNNs to skin disease detection is presented, demonstrating the potential of deep learning algorithms to aid dermatologists and healthcare professionals. An image dataset containing images of various skin diseases is used in the proposed method, including dermatitis, eczema, psoriasis, and other common conditions. High-level representations are captured from input images using a pre- trained CNN (Convolutional Neural Network) model, such as VGG-16. For disease detection, these learned features are fed into a classification layer. The CNN model learns to identify patterns that correspond to the various diseases after being trained on a vast collection of skin photos. An image of skin disease is fed into the model, and the model is trained to extract features that can be used to build a classification model that can accurately identify the disease.