Ensemble of Deep CNN Models for Human Skin Disease Classification

Abstract

Skin diseases are among the leading causes of disability worldwide and are a significant cause of morbidity in sub-Saharan Africa. It can be cured if identified early. Only an expert dermatologist can classify skin disease by examining clinical signs. Sometimes, it can happen that dermatologists do not correctly classify the Skin disease, and therefore prescribe inappropriate drugs to the patient. Various research has been done to automate skin disease classification. Almost all the studies were concentrated on classifying three to four types of skin diseases. Developing a model that can be used in real-world practical AI applications is important. In this study, we present an ensemble model based on the hard-voting scheme of three deep CNN architectures: SKDCNET, FVGG16, and InceptionV3 for automatic classification of the top eight skin diseases. The proposed model utilizes three architectural diversities: training from scratch, fine-tuning, and transfer learning. We used median filter noise removal and data augmentation technique to increase the number of training datasets. The proposed ensemble model produces 98% of accuracy. As an outcome of this study, the proposed model has the potential to be used as a decision support method for dermatologists. It can also contribute to the early identification (treatment) of skin diseases to reduce their further spread.