Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases
Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many image...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6170416 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548906453434368 |
|---|---|
| author | Mosleh Hmoud Al-Adhaileh Ebrahim Mohammed Senan Waselallah Alsaade Theyazn H. H Aldhyani Nizar Alsharif Ahmed Abdullah Alqarni M. Irfan Uddin Mohammed Y. Alzahrani Elham D. Alzain Mukti E. Jadhav |
| author_facet | Mosleh Hmoud Al-Adhaileh Ebrahim Mohammed Senan Waselallah Alsaade Theyazn H. H Aldhyani Nizar Alsharif Ahmed Abdullah Alqarni M. Irfan Uddin Mohammed Y. Alzahrani Elham D. Alzain Mukti E. Jadhav |
| author_sort | Mosleh Hmoud Al-Adhaileh |
| collection | DOAJ |
| description | Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%. |
| format | Article |
| id | doaj-art-9ba1824d66d544d6b73a6c821599b405 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9ba1824d66d544d6b73a6c821599b4052025-02-03T06:12:50ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/61704166170416Deep Learning Algorithms for Detection and Classification of Gastrointestinal DiseasesMosleh Hmoud Al-Adhaileh0Ebrahim Mohammed Senan1Waselallah Alsaade2Theyazn H. H Aldhyani3Nizar Alsharif4Ahmed Abdullah Alqarni5M. Irfan Uddin6Mohammed Y. Alzahrani7Elham D. Alzain8Mukti E. Jadhav9Deanship of E-Learning and Distance Education, King Faisal University, Al Hofuf, Saudi ArabiaDepartment of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, IndiaCollege of Computer Sciences and Information Technology, King Faisal University, Al Hofuf, Saudi ArabiaCommunity College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaDepartment of Computer Engineering and Science, Albaha University, Al Bahah, Saudi ArabiaDepartment of Computer Sciences and Information Technology, Albaha University, Al Bahah, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanDepartment of Computer Sciences and Information Technology, Albaha University, Al Bahah, Saudi ArabiaCommunity College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaShri Shivaji Science & Arts College, Chikhli Dist, Buldana, IndiaCurrently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.http://dx.doi.org/10.1155/2021/6170416 |
| spellingShingle | Mosleh Hmoud Al-Adhaileh Ebrahim Mohammed Senan Waselallah Alsaade Theyazn H. H Aldhyani Nizar Alsharif Ahmed Abdullah Alqarni M. Irfan Uddin Mohammed Y. Alzahrani Elham D. Alzain Mukti E. Jadhav Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases Complexity |
| title | Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases |
| title_full | Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases |
| title_fullStr | Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases |
| title_full_unstemmed | Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases |
| title_short | Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases |
| title_sort | deep learning algorithms for detection and classification of gastrointestinal diseases |
| url | http://dx.doi.org/10.1155/2021/6170416 |
| work_keys_str_mv | AT moslehhmoudaladhaileh deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT ebrahimmohammedsenan deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT waselallahalsaade deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT theyaznhhaldhyani deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT nizaralsharif deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT ahmedabdullahalqarni deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT mirfanuddin deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT mohammedyalzahrani deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT elhamdalzain deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases AT muktiejadhav deeplearningalgorithmsfordetectionandclassificationofgastrointestinaldiseases |