Skin Disease Recognition Method Based on Image Color and Texture Features

Skin diseases, including eczema, acne vulgaris, and psoriasis, are widespread health concerns globally.However, diagnosing these conditions can be difficult due to the intricate variations in skin textures and tones.This research introduces an automated system for classifying skin diseases using machine learning (ML) and image processing techniques.The system utilizes Support Vector Machines (SVM) and Random Forest (RF) classifiers, alongside feature extraction methods such as Hu Moments, Haralick Texture, Color Histogram, and Histogram of Oriented Gradients (HOG).A mobile application was also developed to allow users to upload images and receive real-time predictions.The findings indicate that Random Forest outperforms SVM, achieving an accuracy of 86% with HOG features.This study underscores the potential of ML in making dermatological diagnostics more accessible, especially in resource-limited settings.