Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection

SIGNIFICANCE: The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine. AIM: An approach is proposed for the detection of hepatitis B (HB) virus using a combined Mueller matrix imaging technique and deep learning method. APPROACH: In the proposed approach, Mueller matrix imaging polarimetry is applied to obtain [Formula: see text] Mueller matrix images of 138 HBsAg-containing (positive) serum samples and 136 HBsAg-free (negative) serum samples. The kernel estimation density results show that, of the 16 Mueller matrix elements, elements [Formula: see text] and [Formula: see text] provide the best discriminatory power between the positive and negative samples. RESULTS: As a result, [Formula: see text] and [Formula: see text] are taken as the inputs to five different deep learning models: Xception, VGG16, VGG19, ResNet 50, and ResNet150. It is shown that the optimal classification accuracy (94.5%) is obtained using the VGG19 model with element [Formula: see text] as the input. CONCLUSIONS: Overall, the results confirm that the proposed hybrid Mueller matrix imaging and AI framework provides a simple and effective approach for HB virus detection.