Analyze Skin Histopathology Images Using Multiple Deep Learning Methods

For melanoma diagnosis, visual analysis of skin histopathology images is the gold standard. There have been researches on deep learning-based histopathology image diagnosis. However, few studies explore the use of multiple deep learning methods to analyze histopathology images. Our research used 3 popular deep learning models, and 2 recognized training methods to analyze histopathology images. 312 histopathology images were collected from the department of dermatology in Xiangya Hospital for contrast tests. In the contrast test, three models of ResNet50, InceptionV3, and MobileNet were performed in sequence. Moreover, two training methods of transfer learning method and fully trained method were performed, respectively. Firstly, the result shows that different models with the same training method got similar performance. Secondly, the models trained by the fully trained method (the accuracy of the three models ranged from 99.78% to 99.88%) performance better than the model trained by transfer learning mode (the accuracy of the three models ranged from 96.69% to 96.81%). Therefore, deep learning is suitable for the histopathology image diagnosis of melanoma. For the same deep learning model, the fully trained method is better than transfer learning in histopathology image diagnosis of melanoma.