Traditional skin microscopy heavily relies on the clinical experience of physicians ,and the complexity of skin mi-croscopy images poses significant challenges to clinical diagnosis. In response to this situation ,a polarized skin microscopy system with artificial intelligence-assisted diagnostic functionality has been developed. This system utilizes polarized illumi-nation imaging technology. After magnification through an eyepiece system ,a continuously operating industrial camera cap-tures a large volume of real-time raw skin microscopy data images and transfers them to the host computer. The images are preprocessed using the built-in image processing module in the host computer and are then classified using a trained convolutional neural network ,VGG16 ,for skin microscopy images. Finally ,users can view the diagnostic results through the skin microscopy diagnostic platform. This system allows for a clearer and more effective observation of the morphological characteristics of lesions in deep skin tissues. The classification accuracy of skin microscopy images captured by the system reaches 93.6％,making it suitable for clinical diagnosis and treatment.
维普
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