ACNE8M - An acnes detection and differential diagnosis system using AI technologies

Abstract

Acne is a prevalent skin condition that can lead to serious consequences in severe cases. Traditional treatment requires patients to visit a dermatologist. However, acne diagnosis performed by dermatologists often encounters issues, such as being manual and highly inaccurate. Therefore, there is a need for machinery to assist in the acne diagnosis phase. Numerous image analysis algorithms have been developed using images captured by mobile devices. Nonetheless, most of these algorithms primarily rely on outdated features such as color models or texture-based features, which may result in poor performance when dealing with the intricate nature of acne lesions. Consequently, AI models have been developed for the task of acne detection. However, due to the rarity of high-quality datasets for acne, some of these models have yet to achieve significant results. To overcome these limitations, this paper proposes the ACNE8M, an AI model developed based on the YOLOv8 pre-trained model, to accurately detect seven primary and secondary types of acne lesions, as well as differentiate five additional diagnoses. The model is trained on a well-prepared dataset containing 9,440 images with numerous acne lesions adequately labeled. The results show that the model achieves state-of-the-art performance with a mean Average Precision (mAP) score of 0.69 across the 12 types. The accuracy of detecting each type of acne is impressively high and balanced between the classes, despite the dataset's imbalance caused by the unequal number of images in each acne category. With this study, ACNE8M is expected to provide medical support in the acne diagnosis process and help patients understand their conditions for better treatment.