Dermatological Diagnosis Through Image Analysis and Automated Diagnosis

The fusion of image processing and machine learning has opened new avenues in dermatological disease detection, facilitating accurate diagnosis and treatment. Leveraging a diverse dataset comprising images of various dermatological conditions, including eczema, psoriasis, and acne with 9 diseases, the system prioritizes the analysis of lesion characteristics to enable disease identification. Through advanced image processing techniques, such as feature extraction and texture analysis, coupled with machine learning algorithms like Random Forest and Support Vector Machine, the system achieves impressive accuracy in disease classification. This research not only streamlines the process of diagnosing and understanding dermatological diseases but also holds promise for personalized treatment and healthcare management. The development of a user-friendly web-based application prototype further demonstrates the practicality of the model, providing dermatologists and healthcare professionals with a reliable tool for accurate disease identification. By integrating computer vision with machine learning methodologies, this study presents a comprehensive solution to the challenges of dermatological disease detection, paving the way for enhanced patient care and medical research in dermatology.