Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images
Article
Article Title | Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images |
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ERA Journal ID | 36561 |
Article Category | Article |
Authors | Erdem, Kenan, Kobat, Mehmet Ali, Bilen, Mehmet Nail, Balik, Yunus, Alkan, Sevim, Cavlak, Feyzanur, Poyraz, Ahmet Kursad, Barua, Prabal Datta, Tuncer, Ilknur, Dogan, Sengul, Baygin, Mehmet, Erten, Mehmet, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | International Journal of Imaging Systems and Technology |
Journal Citation | 33 (4), pp. 1144-1159 |
Number of Pages | 16 |
Year | 2023 |
Publisher | John Wiley & Sons |
Place of Publication | United States |
ISSN | 0899-9457 |
1098-1098 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/ima.22914 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/ima.22914 |
Abstract | COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers. |
Keywords | AlexNet; biomedical image classification; transfer learning; Hybrid-Patch-Alex; CT image classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 420308. Health informatics and information systems |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Selcuk University, Turkey |
Firat University Hospital, Turkey | |
Basaksehir Cam and Sakura City Hospital, Turkey | |
Firat University, Turkey | |
University of Southern Queensland | |
University of Technology Sydney | |
Government office in Elazig, Turkiye | |
Erzurum Technical University, Turkey | |
Elazig Fethi Sekin City Hospital, Turkiye | |
Duke-NUS Medical School, Singapore | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z1v85/hybrid-patch-alex-a-new-patch-division-and-deep-feature-extraction-based-image-classification-model-to-detect-covid-19-heart-failure-and-other-lung-conditions-using-medical-images
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