Tumor Detection Method with Traceable Decision Process

Over the last few years, tumor detection with deep learning has attracted ample attention, and various models have been proposed. Some models obtain output images directly from end-to-end models. Other models consist of a complicated network structure. However, it is difficult for doctors to understand the detection procedures of these models. This difficulty hinders their practical implementation. In this paper, therefore, we propose a model that detects tumors in a way that is easy for doctors to understand. The proposed deep neural network model was inspired by a way that doctors interpret images. It consists of two processes: tumor position estimation, and tumor/non-tumor classification. To evaluate the proposed method, we used magnetic resonance images actually collected by a hospital. The output images reveal that the proposed method's procedure of tumor detection is consistent with the way that doctors detect tumors. Likely tumor positions are first estimated. Then, these likely positions are narrowed down to the correct tumor positions. Our proposed method detected tumors with 94.14% accuracy and 93.44% recall.