Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning
This work addresses the problem of accurate and efficient diagnosis of skin conditions for medical applications, representing an incremental improvement over existing methods.
The paper tackled improving skin condition classification by developing a visual symptom checker that combines a pre-trained CNN with a reinforcement learning agent for question-answering, resulting in over 20% higher accuracy compared to a CNN-only approach and up to 10% higher accuracy than a decision tree-based method, while also reducing the average number of questions asked.
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.