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

Abstract

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 $4\times 4$ 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 $M_{22}$ and $M_{33}$ provide the best discriminatory power between the positive and negative samples.

Results: As a result, $M_{22}$ and $M_{33}$ 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 $M_{22}$ 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.

Keywords: HBsAg; Mueller matrix imaging; convolutional neural network; hepatitis B; polarimetry.