| Abstrakt: |
Using computer vision to classify and detect acne lesions, as well as segment wrinkles, is important because of its potential for early skin disease diagnosis and age estimation. The images analyzed in the past mainly included dermoscopy images or face portion images. However, the detection of skin conditions on the face from a face/half body image has never been investigated before. With the success of deep learning in object detection, we propose a deep learning model for acne lesion and wrinkle detection from the face/half body image. The state-of-the-art deep learning networks, Faster Region Convolutional Neural Network, and Residual Network are employed in the study. The features of the image are extracted with 50 layers of Residual Network to produce a conv feature map. From the map, the object proposal is detected with Regional Proposal Network. Finally, the feature map and the proposal are used to classify and localize the region of interest (RoI) in the image. The multiple RoIs in a single image are classified as acne or wrinkle. The detection model achieved a 47.96% mean Average Precision score, therefore, the precision of the proposed method is proven. Instead of using the dermoscopy images to diagnose skin cancer and estimate age, by integrating our proposed model to extract acne or wrinkle from the face/half body image, the diagnosis and estimation works can be analyzed using the face/half body image. [ABSTRACT FROM AUTHOR] |
