Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN
Upper gastrointestinal endoscopy is widely performed to detect early gastric cancers. As an automated detection method for early gastric cancer from endoscopic images, a method involving an object detection model, which is a deep learning technique, was proposed. However, there were challenges regar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/23/11275 |
| _version_ | 1797508101303173120 |
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| author | Atsushi Teramoto Tomoyuki Shibata Hyuga Yamada Yoshiki Hirooka Kuniaki Saito Hiroshi Fujita |
| author_facet | Atsushi Teramoto Tomoyuki Shibata Hyuga Yamada Yoshiki Hirooka Kuniaki Saito Hiroshi Fujita |
| author_sort | Atsushi Teramoto |
| collection | DOAJ |
| description | Upper gastrointestinal endoscopy is widely performed to detect early gastric cancers. As an automated detection method for early gastric cancer from endoscopic images, a method involving an object detection model, which is a deep learning technique, was proposed. However, there were challenges regarding the reduction in false positives in the detected results. In this study, we proposed a novel object detection model, U-Net R-CNN, based on a semantic segmentation technique that extracts target objects by performing a local analysis of the images. U-Net was introduced as a semantic segmentation method to detect early candidates for gastric cancer. These candidates were classified as gastric cancer cases or false positives based on box classification using a convolutional neural network. In the experiments, the detection performance was evaluated via the 5-fold cross-validation method using 1208 images of healthy subjects and 533 images of gastric cancer patients. When DenseNet169 was used as the convolutional neural network for box classification, the detection sensitivity and the number of false positives evaluated on a lesion basis were 98% and 0.01 per image, respectively, which improved the detection performance compared to the previous method. These results indicate that the proposed method will be useful for the automated detection of early gastric cancer from endoscopic images. |
| first_indexed | 2024-03-10T04:57:33Z |
| format | Article |
| id | doaj.art-b77b461b77e44aec9d31ad888c47afe6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:57:33Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b77b461b77e44aec9d31ad888c47afe62023-11-23T02:05:26ZengMDPI AGApplied Sciences2076-34172021-11-0111231127510.3390/app112311275Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNNAtsushi Teramoto0Tomoyuki Shibata1Hyuga Yamada2Yoshiki Hirooka3Kuniaki Saito4Hiroshi Fujita5School of Medical Sciences, Fujita Health University, Toyoake 470-1192, JapanDepartment of Gastroenterology and Hepatology, Fujita Health University, Toyoake 470-1192, JapanDepartment of Gastroenterology and Hepatology, Fujita Health University, Toyoake 470-1192, JapanDepartment of Gastroenterology and Hepatology, Fujita Health University, Toyoake 470-1192, JapanSchool of Medical Sciences, Fujita Health University, Toyoake 470-1192, JapanFaculty of Engineering, Gifu University, Gifu 501-1194, JapanUpper gastrointestinal endoscopy is widely performed to detect early gastric cancers. As an automated detection method for early gastric cancer from endoscopic images, a method involving an object detection model, which is a deep learning technique, was proposed. However, there were challenges regarding the reduction in false positives in the detected results. In this study, we proposed a novel object detection model, U-Net R-CNN, based on a semantic segmentation technique that extracts target objects by performing a local analysis of the images. U-Net was introduced as a semantic segmentation method to detect early candidates for gastric cancer. These candidates were classified as gastric cancer cases or false positives based on box classification using a convolutional neural network. In the experiments, the detection performance was evaluated via the 5-fold cross-validation method using 1208 images of healthy subjects and 533 images of gastric cancer patients. When DenseNet169 was used as the convolutional neural network for box classification, the detection sensitivity and the number of false positives evaluated on a lesion basis were 98% and 0.01 per image, respectively, which improved the detection performance compared to the previous method. These results indicate that the proposed method will be useful for the automated detection of early gastric cancer from endoscopic images.https://www.mdpi.com/2076-3417/11/23/11275gastric cancerendoscopydeep learningconvolutional neural network |
| spellingShingle | Atsushi Teramoto Tomoyuki Shibata Hyuga Yamada Yoshiki Hirooka Kuniaki Saito Hiroshi Fujita Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN Applied Sciences gastric cancer endoscopy deep learning convolutional neural network |
| title | Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN |
| title_full | Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN |
| title_fullStr | Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN |
| title_full_unstemmed | Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN |
| title_short | Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN |
| title_sort | automated detection of gastric cancer by retrospective endoscopic image dataset using u net r cnn |
| topic | gastric cancer endoscopy deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3417/11/23/11275 |
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