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Zhang K, Wang H, Cheng Y, Liu H, Gong Q, Zeng Q, Zhang T, Wei G, Wei Z, Chen D. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Sci Rep 2024; 14:7847. [PMID: 38570595 PMCID: PMC10991264 DOI: 10.1038/s41598-024-58361-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/28/2024] [Indexed: 04/05/2024] Open
Abstract
Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a "Bi-directional feature extraction and fusion module" and a "Purification module for feature channel and space" based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.
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Affiliation(s)
- Kezhi Zhang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Haibao Wang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Yaru Cheng
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Hongyan Liu
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Qi Gong
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Qian Zeng
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Tao Zhang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Guoqiang Wei
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China.
- School of Electronic Engineering, Hunan College of Information, Changsha, 410200, Hunan, China.
| | - Zhi Wei
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China.
| | - Dong Chen
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China.
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Zhang X, Ren Z, Yang C. Center consistency guided multi-view embedding anchor learning for large-scale graph clustering. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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