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A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images
The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600128/ https://www.ncbi.nlm.nih.gov/pubmed/36292006 http://dx.doi.org/10.3390/diagnostics12102316 |
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author | Ramamurthy, Karthik George, Timothy Thomas Shah, Yash Sasidhar, Parasa |
author_facet | Ramamurthy, Karthik George, Timothy Thomas Shah, Yash Sasidhar, Parasa |
author_sort | Ramamurthy, Karthik |
collection | PubMed |
description | The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works. |
format | Online Article Text |
id | pubmed-9600128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96001282022-10-27 A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images Ramamurthy, Karthik George, Timothy Thomas Shah, Yash Sasidhar, Parasa Diagnostics (Basel) Article The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works. MDPI 2022-09-26 /pmc/articles/PMC9600128/ /pubmed/36292006 http://dx.doi.org/10.3390/diagnostics12102316 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ramamurthy, Karthik George, Timothy Thomas Shah, Yash Sasidhar, Parasa A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images |
title | A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images |
title_full | A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images |
title_fullStr | A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images |
title_full_unstemmed | A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images |
title_short | A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images |
title_sort | novel multi-feature fusion method for classification of gastrointestinal diseases using endoscopy images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600128/ https://www.ncbi.nlm.nih.gov/pubmed/36292006 http://dx.doi.org/10.3390/diagnostics12102316 |
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