A novel multi-feature learning model for disease diagnosis using face skin images

Facial skin characteristics can provide valuable information about a patient's underlying health conditions. In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses. To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids. Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF). Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.