The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment
According to the Global Cancer Statistics 2020 published in the official journal of the American Cancer Society (ACS), colorectal cancer ranked 4th in incidence and 2nd in mortality, and the 2018 Cancer Registry Report of Taiwan Health Promotion Administration showed that colorectal cancer ranked 2n...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/9089528 |
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| author | Te-Jen Su Feng-Chun Lee Cheuk-Kwan Sun Fu-Xiang Ke Shih-Ming Wang Ming-Chih Huang |
| author_facet | Te-Jen Su Feng-Chun Lee Cheuk-Kwan Sun Fu-Xiang Ke Shih-Ming Wang Ming-Chih Huang |
| author_sort | Te-Jen Su |
| collection | DOAJ |
| description | According to the Global Cancer Statistics 2020 published in the official journal of the American Cancer Society (ACS), colorectal cancer ranked 4th in incidence and 2nd in mortality, and the 2018 Cancer Registry Report of Taiwan Health Promotion Administration showed that colorectal cancer ranked 2nd in incidence and 3rd in mortality. With the rapid evolution of the times, the lifestyles of the people have shifted from what they used to be. In addition to uncontrollable factors such as family genetic disorders, diet, and bad habits, life stress may lead to an unhealthy body mass index (BMI), which, together with aging, increases the incidence of colorectal cancer. In this study, the convolutional neural network was used to assess the risk of tumor in the colon by colonoscopy. The endoscopic images of the colon, which were classified into three categories of healthy (normal), benign tumor, and malignant tumor, were adopted as training data. When this method is combined with the patient’s physical data, the risk cancer can be calculated by the fuzzy algorithm. Based on the result of this study, the accuracy of the tumor profile by colonoscopy, that is, 81.6%, is more precise than that of colorectal cancer tumor analysis studies in the recent literature. The proposed method will help physicians in the diagnosis of colorectal cancer and treatment decisions. |
| format | Article |
| id | doaj-art-39e9ff8bc39844b0a3559e54f4b31763 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-39e9ff8bc39844b0a3559e54f4b317632025-02-03T01:32:36ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/9089528The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor AssessmentTe-Jen Su0Feng-Chun Lee1Cheuk-Kwan Sun2Fu-Xiang Ke3Shih-Ming Wang4Ming-Chih Huang5Department of ElectronicsDepartment of ElectronicsDepartment of Emergency MedicineDepartment of ElectronicsDepartment of Computer Science and Information EngineeringDepartment of ElectronicsAccording to the Global Cancer Statistics 2020 published in the official journal of the American Cancer Society (ACS), colorectal cancer ranked 4th in incidence and 2nd in mortality, and the 2018 Cancer Registry Report of Taiwan Health Promotion Administration showed that colorectal cancer ranked 2nd in incidence and 3rd in mortality. With the rapid evolution of the times, the lifestyles of the people have shifted from what they used to be. In addition to uncontrollable factors such as family genetic disorders, diet, and bad habits, life stress may lead to an unhealthy body mass index (BMI), which, together with aging, increases the incidence of colorectal cancer. In this study, the convolutional neural network was used to assess the risk of tumor in the colon by colonoscopy. The endoscopic images of the colon, which were classified into three categories of healthy (normal), benign tumor, and malignant tumor, were adopted as training data. When this method is combined with the patient’s physical data, the risk cancer can be calculated by the fuzzy algorithm. Based on the result of this study, the accuracy of the tumor profile by colonoscopy, that is, 81.6%, is more precise than that of colorectal cancer tumor analysis studies in the recent literature. The proposed method will help physicians in the diagnosis of colorectal cancer and treatment decisions.http://dx.doi.org/10.1155/2022/9089528 |
| spellingShingle | Te-Jen Su Feng-Chun Lee Cheuk-Kwan Sun Fu-Xiang Ke Shih-Ming Wang Ming-Chih Huang The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment Discrete Dynamics in Nature and Society |
| title | The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment |
| title_full | The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment |
| title_fullStr | The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment |
| title_full_unstemmed | The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment |
| title_short | The Application of Convolutional Neural Network Combined with Fuzzy Algorithm in Colorectal Endoscopy for Tumor Assessment |
| title_sort | application of convolutional neural network combined with fuzzy algorithm in colorectal endoscopy for tumor assessment |
| url | http://dx.doi.org/10.1155/2022/9089528 |
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