Explainable health prediction from facial features with transfer learning

Autor: Tee Connie, Yee Fan Tan, Michael Kah Ong Goh, Hock Woon Hon, Zulaikha Kadim, Li Pei Wong
Rok vydání: 2022
Předmět:
Zdroj: Journal of Intelligent & Fuzzy Systems. 42:2491-2503
ISSN: 1875-8967
1064-1246
DOI: 10.3233/jifs-211737
Popis: In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.
Databáze: OpenAIRE
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