GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images
Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particular...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-08-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024300 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590828689686528 |
|---|---|
| author | Sonam Aggarwal Isha Gupta Ashok Kumar Sandeep Kautish Abdulaziz S. Almazyad Ali Wagdy Mohamed Frank Werner Mohammad Shokouhifar |
| author_facet | Sonam Aggarwal Isha Gupta Ashok Kumar Sandeep Kautish Abdulaziz S. Almazyad Ali Wagdy Mohamed Frank Werner Mohammad Shokouhifar |
| author_sort | Sonam Aggarwal |
| collection | DOAJ |
| description | Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew's Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. |
| format | Article |
| id | doaj-art-9032ca46eee542d1a07eaaefc0f1d119 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-9032ca46eee542d1a07eaaefc0f1d1192025-01-23T07:47:47ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-08-012186847686910.3934/mbe.2024300GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic imagesSonam Aggarwal0Isha Gupta1Ashok Kumar2Sandeep Kautish3Abdulaziz S. Almazyad4Ali Wagdy Mohamed5Frank Werner6Mohammad Shokouhifar7Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaModel Institute of Engineering and Technology, Jammu, J&K, IndiaChandigarh University, Mohali, Punjab 140413 IndiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaOperations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, EgyptFaculty of Mathematics, Otto-von-Guericke University, Magdeburg 39016, GermanyInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamConvolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew's Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%.https://www.aimspress.com/article/doi/10.3934/mbe.2024300deep learningconvolutional neural networkmedical image analysiscomputer-aided diagnosisartificial intelligence |
| spellingShingle | Sonam Aggarwal Isha Gupta Ashok Kumar Sandeep Kautish Abdulaziz S. Almazyad Ali Wagdy Mohamed Frank Werner Mohammad Shokouhifar GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images Mathematical Biosciences and Engineering deep learning convolutional neural network medical image analysis computer-aided diagnosis artificial intelligence |
| title | GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images |
| title_full | GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images |
| title_fullStr | GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images |
| title_full_unstemmed | GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images |
| title_short | GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images |
| title_sort | gastrofuse net an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images |
| topic | deep learning convolutional neural network medical image analysis computer-aided diagnosis artificial intelligence |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024300 |
| work_keys_str_mv | AT sonamaggarwal gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT ishagupta gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT ashokkumar gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT sandeepkautish gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT abdulazizsalmazyad gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT aliwagdymohamed gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT frankwerner gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages AT mohammadshokouhifar gastrofusenetanensembledeeplearningframeworkdesignedforgastrointestinalabnormalitydetectioninendoscopicimages |