Improving Skin Condition Classification with a Visual Symptom Checker
  Trained using Reinforcement Learning
  We present a visual symptom checker that combines a pre-trained Convolutional
Neural Network (CNN) with a Reinforcement Learning (RL) agent as a Question
Answering (QA) model. This method increases the classification confidence and
accuracy of the visual symptom checker, and decreases the average number of
questions asked to narrow down the differential diagnosis. A Deep Q-Network
(DQN)-based RL agent learns how to ask the patient about the presence of
symptoms in order to maximize the probability of correctly identifying the
underlying condition. The RL agent uses the visual information provided by CNN
in addition to the answers to the asked questions to guide the QA system. We
demonstrate that the RL-based approach increases the accuracy more than 20%
compared to the CNN-only approach, which only uses the visual information to
predict the condition. Moreover, the increased accuracy is up to 10% compared
to the approach that uses the visual information provided by CNN along with a
conventional decision tree-based QA system. We finally show that the RL-based
approach not only outperforms the decision tree-based approach, but also
narrows down the diagnosis faster in terms of the average number of asked
questions.
