Computer Science developing acne detection system using Deep Learning Algorithm

Acne is a common skin condition affecting individuals globally, and accurate, efficient detection is crucial for effective treatment. This project develops an acne detection system using deep learning techniques to automatically classify various types of acne and related skin conditions. The system utilizes the YOLO V9 algorithm, known for its speed and accuracy in object detection, to identify and classify 15 distinct categories of acne, including pimples, acne scars, blackheads, cystic acne, and others. The front-end of the application is developed using Streamlit, providing an interactive and user-friendly interface for real-time acne detection. The model is trained on a comprehensive dataset containing labeled images of various acne types, enabling the system to accurately predict and classify each type based on visual features. This solution aims to assist dermatologists and healthcare providers in making faster and more precise diagnoses. By leveraging the power of deep learning algorithms like YOLO V9, the system offers a scalable, efficient, and automated approach to acne detection, contributing to the advancement of personalized skincare.

Keywords: Acne Detection, YOLO V9, Deep Learning, Streamlit, Skin Conditions, Object Detection, Image Classification, Dermatology, Computer Vision, Automated Diagnosis, Healthcare Technology, Real-Time Prediction.