Diagnosis Support Model of Cardiomegaly Based on CNN Using ResNet and Explainable Feature Map

Medical expert support systems using medical data significantly contribute to the field of medicine by utilizing intelligent image analysis technology. Such medical systems must be validated and must have a transparent internal structure. Therefore, the technique of analyzing the results of the medical system is an important factor. This study proposes a diagnosis support model of cardiomegaly based on CNN using ResNet and explainable feature map. To configure the model, initially, a cardiomegaly diagnosis model is configured using ResNet, and a chest X-ray data set is used and learned. As the result changes resulting from the input changes of the model configured for model diagnosis support are detected, an explainable feature map for analysis is implemented. The input changes of the model are made as each pixel of the required image is reversed in sequence and the changes of the neural network output layer are saved on the feature map. As a result, the configured analysis method provides information inside the neural network through an accuracy result close to 80% and a visually expressed feature map. In particular, this interpretation method can be extended to a more general form in a medical support system that analyzes patterns.