Smart Dermatology: Machine Learning Approaches for Skin Disease Identification

Abstract

Skin conditions are among the most common health issues, affecting people for various reasons—such as bacterial or viral infections, allergies, or fungal growths. While laser and photonics-based technologies have made diagnosis faster and more precise, these methods are still costly and not accessible to everyone. That’s where image processing and deep learning step in. By using just, a digital image of the affected skin area, along with a computer and a camera, we can build a low-cost, efficient dermatology screening system. Our method focuses on using pre-trained Convolutional Neural Networks (CNNs) to extract features after resizing the image. This eliminates the need for expensive equipment and speeds up diagnosis. Traditional diagnostic approaches like visual inspection or biopsy are not only time-consuming but can sometimes be inaccurate. We explore machine learning (ML) techniques like SVMs and deep learning models such as ResNet, EfficientNet, and MobileNet using benchmark datasets like ISIC. By applying preprocessing techniques like augmentation and feature extraction, the system's accuracy improves significantly. Experimental results show that CNN-based models consistently outperform older ML approaches in detecting melanoma. This demonstrates how AI-driven solutions can support dermatologists in making faster and more accurate diagnoses, ultimately leading to improved patient outcomes.