Convolutional Neural Network with Feature Extraction to Improve the Classification Accuracy of Multi-Class Facial Skin Disorders

Abstract

This study aims to improve the accuracy of multi-class facial skin disorder classification using a convolutional neural network (CNN) enhanced with feature extraction. The CNN method for classifying multi-class facial skin disorders uses color feature extraction using color moment (CM) and Laplacian of Gaussian (LoG) for direct shape with image data. Multi-class facial skin disorders include oily, hyperpigmentation, acne, redness, blackhead, and normal. A public dataset is used with 7151 images with a balanced number of data classes. Researchers divided the data set into 80% for training and 20% for testing. Experiments are carried out through training and testing with 100 epochs, resulting in an accuracy of 85% for CNN, 66% for the CM-CNN, 80% for LoG-CNN, and 91% for CM-LoG-CNN. The highest classification accuracy is achieved with the CM-LoG-CNN combination.