Acne Vulgaris Detection and Classification: A Dual Integrated Deep CNN Model

Recognizing acne disease and evaluating its type is vital for the efficacy of the medical treatment. This report collects a dataset of 420 images and then labels them into seven different classes by a well-experienced dermatologist. After a pre-processing step, including local and global contrast enhancement and noise removal by a smoothing filter, the dataset size is enhanced using augmentation. The images of the dataset and the augmented ones are all fed into a novel integrated dual deep convolutional neural network (CNN) model to recognize acne disease and its type by classifying it into seven groups. First, two CNN-based units are designed to extract deep feature maps, later combined in a feature aggregation module. The aggregated features provide rich input…