Abstract

A novel method of artificial intelligence (AI) classification is proposed for hepatitis B virus (HBV) detection based on the Mueller matrix imaging system. The feasibility of the proposed technique is demonstrated by measuring the optical properties of non-infected and infected HBV blood samples. Furthermore, different AI classifier techniques namely Yolo5, Yolo5-Restnet101, Yolo5-EfficientnetB0, and Yolo5-MobilenetV2 have been employed to classify the HBV samples. The results show that the proposed method provides 99% accuracy for HBV classification. In general, the proposed technique provides reliable and simple devices for HBV diagnosis applications.

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