Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network

• Main Authors: Belal Ahmad, Mohd Usama, Chuen-Min Huang, Kai Hwang, M. Shamim Hossain, Ghulam Muhammad
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Discriminative feature learning; convolutional neural networks; triplet loss function; skin disease
• Online Access: https://ieeexplore.ieee.org/document/9007648/

Nowadays, skin disease among humans has been a common disease, especially in America millions of people are suffering from various kinds of skin disease. Usually, these diseases have hidden dangers which lead to human not only lack of self-confidence and psychological depression but also a risk of skin cancer. Diagnosis of these kinds of diseases usually required medical experts with high-level instruments due to a lack of visual resolution in skin disease images. Moreover, manual diagnosis of skin disease is often subjective, time-consuming, and required more human effort. Thus, there is a need to develop a computer-aided system that automatically diagnoses the skin disease problem. Moreover, most of the existing works in skin disease used convolutional neural networks (CNN) with classical loss functions, which limit the model to learn discriminative features from skin images. Thus to address the above mention problem we proposed a new framework by fine-tuning layers of ResNet152 and InceptionResNet-V2 models with a triplet loss function. In the proposed framework, first, we learning the embedding from input images into Euclidean space by using deep CNN ResNet152 and InceptionResNet-V2 model. Second, we compute L-2 distance among corresponding images from euclidean space to learn discriminative features of skin disease images by using triplet loss function. Finally, classify the input images using these L-2 distances. Human face skin disease images used in the proposed framework are acquired from the Hospital in Wuhan China. Experiment results and their analysis shows the effectiveness of the proposed framework which achieve better accuracy than many existing works in skin disease tasks.