Automatic Detection of Colorectal Polyps Using Transfer Learning
Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/17/5704 |
| _version_ | 1797520866150449152 |
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| author | Eva-H. Dulf Marius Bledea Teodora Mocan Lucian Mocan |
| author_facet | Eva-H. Dulf Marius Bledea Teodora Mocan Lucian Mocan |
| author_sort | Eva-H. Dulf |
| collection | DOAJ |
| description | Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms. |
| first_indexed | 2024-03-10T08:04:38Z |
| format | Article |
| id | doaj.art-1c037e8589b5424faad32157a2176fa3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:04:38Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1c037e8589b5424faad32157a2176fa32023-11-22T11:11:05ZengMDPI AGSensors1424-82202021-08-012117570410.3390/s21175704Automatic Detection of Colorectal Polyps Using Transfer LearningEva-H. Dulf0Marius Bledea1Teodora Mocan2Lucian Mocan3Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, RomaniaDepartment of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, RomaniaDepartment of Physiology, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, RomaniaDepartment of Surgery, 3-rd Surgery Clinic, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, RomaniaColorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.https://www.mdpi.com/1424-8220/21/17/5704colorectal cancercomputer aided decision support systemartificial intelligence |
| spellingShingle | Eva-H. Dulf Marius Bledea Teodora Mocan Lucian Mocan Automatic Detection of Colorectal Polyps Using Transfer Learning Sensors colorectal cancer computer aided decision support system artificial intelligence |
| title | Automatic Detection of Colorectal Polyps Using Transfer Learning |
| title_full | Automatic Detection of Colorectal Polyps Using Transfer Learning |
| title_fullStr | Automatic Detection of Colorectal Polyps Using Transfer Learning |
| title_full_unstemmed | Automatic Detection of Colorectal Polyps Using Transfer Learning |
| title_short | Automatic Detection of Colorectal Polyps Using Transfer Learning |
| title_sort | automatic detection of colorectal polyps using transfer learning |
| topic | colorectal cancer computer aided decision support system artificial intelligence |
| url | https://www.mdpi.com/1424-8220/21/17/5704 |
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