Gastric polyp detection module based on improved attentional feature fusion
Abstract Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-023-01130-x |
| _version_ | 1797774030123565056 |
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| author | Yun Xie Yao Yu Mingchao Liao Changyin Sun |
| author_facet | Yun Xie Yao Yu Mingchao Liao Changyin Sun |
| author_sort | Yun Xie |
| collection | DOAJ |
| description | Abstract Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected. |
| first_indexed | 2024-03-12T22:15:01Z |
| format | Article |
| id | doaj.art-81a55db7f7cf41cf9445b3bdc8b820a5 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-12T22:15:01Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-81a55db7f7cf41cf9445b3bdc8b820a52023-07-23T11:20:58ZengBMCBioMedical Engineering OnLine1475-925X2023-07-0122112310.1186/s12938-023-01130-xGastric polyp detection module based on improved attentional feature fusionYun Xie0Yao Yu1Mingchao Liao2Changyin Sun3School of Intelligence Science and technology, University of Science and Technology BeijingSchool of Intelligence Science and technology, University of Science and Technology BeijingSchool of Intelligence Science and technology, University of Science and Technology BeijingSchool of Artificial Intelligence, Anhui UniversityAbstract Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected.https://doi.org/10.1186/s12938-023-01130-xGastric cancerDeep learningPolyp detectionAttention moduleFeature fusion |
| spellingShingle | Yun Xie Yao Yu Mingchao Liao Changyin Sun Gastric polyp detection module based on improved attentional feature fusion BioMedical Engineering OnLine Gastric cancer Deep learning Polyp detection Attention module Feature fusion |
| title | Gastric polyp detection module based on improved attentional feature fusion |
| title_full | Gastric polyp detection module based on improved attentional feature fusion |
| title_fullStr | Gastric polyp detection module based on improved attentional feature fusion |
| title_full_unstemmed | Gastric polyp detection module based on improved attentional feature fusion |
| title_short | Gastric polyp detection module based on improved attentional feature fusion |
| title_sort | gastric polyp detection module based on improved attentional feature fusion |
| topic | Gastric cancer Deep learning Polyp detection Attention module Feature fusion |
| url | https://doi.org/10.1186/s12938-023-01130-x |
| work_keys_str_mv | AT yunxie gastricpolypdetectionmodulebasedonimprovedattentionalfeaturefusion AT yaoyu gastricpolypdetectionmodulebasedonimprovedattentionalfeaturefusion AT mingchaoliao gastricpolypdetectionmodulebasedonimprovedattentionalfeaturefusion AT changyinsun gastricpolypdetectionmodulebasedonimprovedattentionalfeaturefusion |