In Journal of biomedical optics
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 × 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.
Pham Thi-Thu-Hien, Nguyen Hoang-Phuoc, Luu Thanh-Ngan, Le Ngoc-Bich, Vo Van-Toi, Huynh Ngoc-Trinh, Phan Quoc-Hung, Le Thanh-Hai
2022-Jul
HBsAg, Mueller matrix imaging, convolutional neural network, hepatitis B, polarimetry
