Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.
Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medic...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292601&type=printable |
| _version_ | 1797347771508850688 |
|---|---|
| author | Muhammad Ramzan Mudassar Raza Muhammad Irfan Sharif Faisal Azam Jungeun Kim Seifedine Kadry |
| author_facet | Muhammad Ramzan Mudassar Raza Muhammad Irfan Sharif Faisal Azam Jungeun Kim Seifedine Kadry |
| author_sort | Muhammad Ramzan |
| collection | DOAJ |
| description | Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset. |
| first_indexed | 2024-03-08T11:52:37Z |
| format | Article |
| id | doaj.art-ad4d31466cb644fd924ace233ed243db |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T11:52:37Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-ad4d31466cb644fd924ace233ed243db2024-01-24T05:30:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011810e029260110.1371/journal.pone.0292601Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.Muhammad RamzanMudassar RazaMuhammad Irfan SharifFaisal AzamJungeun KimSeifedine KadryComputer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292601&type=printable |
| spellingShingle | Muhammad Ramzan Mudassar Raza Muhammad Irfan Sharif Faisal Azam Jungeun Kim Seifedine Kadry Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. PLoS ONE |
| title | Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. |
| title_full | Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. |
| title_fullStr | Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. |
| title_full_unstemmed | Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. |
| title_short | Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. |
| title_sort | gastrointestinal tract disorders classification using ensemble of inceptionnet and proposed gitnet based deep feature with ant colony optimization |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292601&type=printable |
| work_keys_str_mv | AT muhammadramzan gastrointestinaltractdisordersclassificationusingensembleofinceptionnetandproposedgitnetbaseddeepfeaturewithantcolonyoptimization AT mudassarraza gastrointestinaltractdisordersclassificationusingensembleofinceptionnetandproposedgitnetbaseddeepfeaturewithantcolonyoptimization AT muhammadirfansharif gastrointestinaltractdisordersclassificationusingensembleofinceptionnetandproposedgitnetbaseddeepfeaturewithantcolonyoptimization AT faisalazam gastrointestinaltractdisordersclassificationusingensembleofinceptionnetandproposedgitnetbaseddeepfeaturewithantcolonyoptimization AT jungeunkim gastrointestinaltractdisordersclassificationusingensembleofinceptionnetandproposedgitnetbaseddeepfeaturewithantcolonyoptimization AT seifedinekadry gastrointestinaltractdisordersclassificationusingensembleofinceptionnetandproposedgitnetbaseddeepfeaturewithantcolonyoptimization |