Recognizing acne Vulgaris severity levels: An application of faster R-CNN and YOLO methods on medical images

Acne Vulgaris is a form of acne that most commonly affects around 85% of adolescents. Most of them need treatment immediately by a dermatologist because it can occur unavoidable scar after severe inflammatory acne. In addition, acne diagnosis by a dermatologist could be difficult and time-consuming, so computer vision technology can be a good approach for the solution using any optical sensors, such as a digital camera or smartphone camera. This study implements two deep learning methods, namely Faster R-CNN and YOLO, to detect acne objects from images and classify them into some severity levels that can be used by dermatologists for consistently assessing clinical practice trials. The method comparison results show that the Faster R-CNN model achieves better accuracy than YOLO for acne object recognition and severity classification with an average confidence score are 79% and 33%, respectively. To provide a user-interactive system, a web application has been applied to be used by dermatologists. Moreover, the results in Acne Vulgaris severity levels detection have been tested and confirmed by dermatologist experts.