Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence

Abstract

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease's complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models' opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.

Keywords: And layer-wise relevance propagation (LRP); Artificial intelligence (AI); Chickenpox; Deep learning (DL); Explainable artificial intelligence (XAI); Machine learning (ML); Measles; Monkeypox; Transfer learning (TL); VGG16.

Fig. 1

Skin images of four classes: (a) Chickenpox; (b) Measles; (c) Monkeypox; (d) Normal.

Fig. 2

Framework of the proposed model.

Fig. 3

VGG16 original architecture.

Fig. 4

Modified VGG16 architecture.

Fig. 5

Testing confusion matrix for the proposed model.

Fig. 6

Explanations based on LRP for the VGG16 model.