Skin Disease Prediction Using Deep Learning

Abstract

Skin diseases pose a major global health concern, impacting individuals of all ages and demographics. Traditional diagnostic approaches rely heavily on dermatologists' visual assessments, which can be time-intensive and subjective, potentially causing delays in treatment and an increased risk of misdiagnosis. These challenges are especially pronounced in remote or underserved regions with limited access to specialized healthcare. To overcome these issues, this study introduces an automated skin disease prediction system powered by deep learning, specifically utilizing the VGG16 convolutional neural network. The system incorporates a robust data preprocessing pipeline involving image normalization, resizing, and augmentation to enhance training efficiency. Through transfer learning, a VGG16 on a curated dataset to extract key features for distinguishing different skin conditions. Custom connected layers then distinguish images into specific diagnosis. Designed as seamless integration into web and mobile applications, this system enables real-time diagnostics, assisting healthcare professionals and patients alike in improving accessibility and treatment outcomes.