A Combination of Dnn And Bn for Automatic Skin Disease Diagnosis

Artificial intelligence aims to mimic human intelligence by giving machines the ability to perceive and think. However, current AI is on the horns of a dilemma. Tools like deep learning are good at finding similarities but cannot make reasonable inferences like humans. Their inability to integrate with human knowledge also requires large amounts of input data for tuning. In this paper, we propose an architecture that combines deep neural networks as nodes into Bayesian networks, which combines human knowledge with the perceptual results of deep learning tools. Using Bayesian networks for inference provides good interpretability and acceptable training data requirements. This architecture can correlate symptoms, demographic information, and convolutional neural networks in dermatological diagnosis. We conducted experiments on the ISIC 2019: Training dataset. With the help of dermatologists' expert knowledge, the architecture achieves an overall diagnostic accuracy of 88.1%, which is 23.6% higher than the pure deep learning approach.