# Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN
## 4 Discussion  
In this study, we proposed a U-Net RCNN that combines U-Net and an FP reduction method for object detection and performs automatic detection of GC cases. Using the output images of U-Net, individual candidate regions were recognized by conventional thresholding and labeling techniques and bounding boxes were obtained. To eliminate FPs, candidate regions were classified as true GC cases and FPs by CNN. The lesion-based sensitivity for initial detection by this method was 0.989, while the number of FPs per image was 0.035, which was much better than the previous study (sensitivity, 0.957; number of FPs per image, 0.105). The Mask R-CNN introduced in our  
Figure 6: Examples of false positive reduction: (**a,b**) false positives correctly eliminated by the proposed method; (**c,d**) FPs that were not eliminated by the proposed method; (**e,f**) gastric cancer lesions eliminated by false positive reduction. Arrows indicate the labeled regions. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Evaluation Using All Gastric Cancer Cases** & **Evaluation Using the Detected Gastric Cancer Cases** \\ \hline \multirow{2}{*}{Previous method [16]} & **Di** & **Ji** & **Di** & **Ji** \\ \hline Previous method [16] & 0.542 & 0.371 & 0.720 & 0.494 \\ Proposed method & **0.555** & **0.427** & 0.602 & 0.463 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of [PERSON] and [PERSON] for cancer segmentation. previous method was able to accurately detect visually distinct objects such as unevenness due to the principles of the object detection model; however, it was difficult to detect subtle changes in the mucosal surface. On the other hand, U-Net, which was employed in this study, could analyze local regions in an image. A detailed analysis of the gastric mucosa in endoscopic images was performed and patterns that differed from normal were accurately recognized. In the second stage, we compared the performance of six different CNN architectures and found that DenseNet169 showed the best performance, reducing FPs by approximately 30% to 0.011, while maintaining a lesion-based detection sensitivity of 0.989. When evaluated in an image-based manner, the detection sensitivity dropped by approximately 4% from 0.942 to 0.897. As shown in Figure 5e,f, most of the images that remained undetected were taken from angles and distances that were difficult to see, and other images in the case were able to compensate for the detection. The accuracy of extracting the invasive region of GC was evaluated by [PERSON] and [PERSON] and the results were 0.55 and 0.42, respectively, for all GC images; and 0.60 and 0.46, respectively, when the evaluation was limited to the correctly detected images. The proposed method was more accurate than the previous study using Mask R-CNN when evaluating all GC images, while the previous study was more accurate when evaluating only the detected regions. This indicates that our method may detect subtle lesions but is not able to extract the exact shapes. To improve the extraction accuracy, it is necessary to improve the CNN model used for the initial detection and to add post-processing, such as region growing, to the extracted images. Because the proposed method provides a sensitivity of 98% in detecting GC while keeping FPs at an acceptable level, it may be useful for maintaining high examination accuracy in screening for GC by covering differences in the experience of physicians. Although we could not compare our results accurately because a different dataset was used, the proposed method using U-Net and FP reduction techniques had a better detection sensitivity than those in previous studies using a SSD [14] and Mask R-CNN [16]. Furthermore, a previous study using SSD detected lesions with a bounding box, whereas the proposed method segments the GC regions. The detection and segmentation capabilities of the proposed method are significantly improved compared to the previous methods. The major limitation of the proposed method is the small number of images.