Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets

dc.authorid kadhim, yezi ali/0000-0002-1111-8202
dc.authorid Mishra, Alok/0000-0003-1275-2050
dc.authorid Khan, Muhammad/0000-0002-9195-3477
dc.authorscopusid 57214819564
dc.authorscopusid 57209876827
dc.authorscopusid 7201441575
dc.authorwosid kadhim, yezi ali/ADW-8078-2022
dc.authorwosid Mishra, Alok/AAE-2673-2019
dc.authorwosid Khan, Muhammad/N-5478-2016
dc.contributor.author Kadhim, Yezi Ali
dc.contributor.author Khan, Muhammad Umer
dc.contributor.author Mishra, Alok
dc.contributor.other Software Engineering
dc.contributor.other Mechatronics Engineering
dc.date.accessioned 2024-07-05T15:24:01Z
dc.date.available 2024-07-05T15:24:01Z
dc.date.issued 2022
dc.department Atılım University en_US
dc.department-temp [Kadhim, Yezi Ali] Atilim Univ, Dept Modeling & Design Engn Syst MODES, TR-06830 Ankara, Turkey; [Kadhim, Yezi Ali] Atilim Univ, Dept Elect & Elect Engn, TR-06830 Ankara, Turkey; [Khan, Muhammad Umer] Atilim Univ, Dept Mechatron Engn, TR-06830 Ankara, Turkey; [Mishra, Alok] Atilim Univ, Dept Software Engn, TR-06830 Ankara, Turkey; [Mishra, Alok] Molde Univ Coll Specialized Univ Logist, Informat & Digitalizat Grp, N-6410 Molde, Norway en_US
dc.description kadhim, yezi ali/0000-0002-1111-8202; Mishra, Alok/0000-0003-1275-2050; Khan, Muhammad/0000-0002-9195-3477 en_US
dc.description.abstract Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors. en_US
dc.description.sponsorship Molde University College-Specialized Univ. in Logistics en_US
dc.description.sponsorship Authors thanks to the Molde University College-Specialized Univ. in Logistics, for the support of Open access fund. en_US
dc.identifier.citationcount 7
dc.identifier.doi 10.3390/s22228999
dc.identifier.issn 1424-8220
dc.identifier.issue 22 en_US
dc.identifier.pmid 36433595
dc.identifier.scopus 2-s2.0-85142718337
dc.identifier.uri https://doi.org/10.3390/s22228999
dc.identifier.uri https://hdl.handle.net/20.500.14411/2374
dc.identifier.volume 22 en_US
dc.identifier.wos WOS:000887656700001
dc.identifier.wosquality Q2
dc.institutionauthor Khan, Muhammad Umer
dc.institutionauthor Mıshra, Alok
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 28
dc.subject deep learning en_US
dc.subject CNN en_US
dc.subject auto-encoder en_US
dc.subject ant colony optimization en_US
dc.subject COVID-19 en_US
dc.subject brain tumor en_US
dc.title Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets en_US
dc.type Article en_US
dc.wos.citedbyCount 15
dspace.entity.type Publication
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