Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases
It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagn...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-02-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1919.pdf |
| _version_ | 1831867881929834496 |
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| author | Soner Kiziloluk Muhammed Yildirim Harun Bingol Bilal Alatas |
| author_facet | Soner Kiziloluk Muhammed Yildirim Harun Bingol Bilal Alatas |
| author_sort | Soner Kiziloluk |
| collection | DOAJ |
| description | It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%. |
| first_indexed | 2024-03-07T19:00:50Z |
| format | Article |
| id | doaj.art-86f1b242860243249522d1365176a176 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2025-03-21T18:45:51Z |
| publishDate | 2024-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-86f1b242860243249522d1365176a1762024-06-06T15:06:39ZengPeerJ Inc.PeerJ Computer Science2376-59922024-02-0110e191910.7717/peerj-cs.1919Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseasesSoner Kiziloluk0Muhammed Yildirim1Harun Bingol2Bilal Alatas3Computer Engineering, Malatya Turgut Ozal University, Malatya, TurkeyComputer Engineering, Malatya Turgut Ozal University, Malatya, TurkeySoftware Engineering, Malatya Turgut Ozal University, Malatya, TurkeySoftware Engineering, Firat (Euphrates) University, Elazig, TurkeyIt is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.https://peerj.com/articles/cs-1919.pdfArtificial intelligenceClassificationDandelion optimizerEndoscopic imagesGastrointestinal diseases |
| spellingShingle | Soner Kiziloluk Muhammed Yildirim Harun Bingol Bilal Alatas Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases PeerJ Computer Science Artificial intelligence Classification Dandelion optimizer Endoscopic images Gastrointestinal diseases |
| title | Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases |
| title_full | Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases |
| title_fullStr | Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases |
| title_full_unstemmed | Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases |
| title_short | Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases |
| title_sort | multi feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases |
| topic | Artificial intelligence Classification Dandelion optimizer Endoscopic images Gastrointestinal diseases |
| url | https://peerj.com/articles/cs-1919.pdf |
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