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Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions. [ABSTRACT FROM AUTHOR]

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions.