Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1006
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Msonda, Pike | - |
dc.contributor.author | Uymaz, Sait Ali | - |
dc.contributor.author | Karaağaç, Seda Soğukpınar | - |
dc.date.accessioned | 2021-12-13T10:32:19Z | - |
dc.date.available | 2021-12-13T10:32:19Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 0765-0019 | - |
dc.identifier.issn | 1958-5608 | - |
dc.identifier.uri | https://doi.org/10.18280/ts.370620 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1006 | - |
dc.description.abstract | In recent decades, automatic diagnosis using machine-learning techniques have been the focus of research. Mycobacterium Tuberculosis (TB) is a deadly disease that has plagued most developing countries presents a problem that can be tackled by automatic diagnosis. The World Health Organization (WHO) set years 2030 and 2035 as milestones for a significant reduction in new infections and deaths although lack of well-trained professionals and insufficient or fragile public health systems (in developing countries) are just some of the major factors that have slowed the eradication of the TB endemic. Deep convolutional neural networks (DCNNs) have demonstrated remarkable results across problem domains dealing with grid-like data (i.e., images and videos). Traditionally, a methodology for detecting TB is through radiology combined with previous success DCNN have achieved in image classification makes them the perfect candidate to classify Chest X-Ray (CXR) images. In this study, we propose three types of DCNN trained using two public datasets and another new set which we collected from Konya Education and Research Hospital, Konya, Turkey. Also, the DCNN architectures were integrated with an extra layer called Spatial Pyramid Pooling (SPP) a methodology that equips convolutional neural networks with the ability for robust feature pooling by using spatial bins. The result indicates the potential for an automated system to diagnose tuberculosis with accuracies above a radiologist professional. | en_US |
dc.description.sponsorship | Coordinators of Scientific Research Projects of Konya Technical University [191013018] | en_US |
dc.description.sponsorship | This work was financially supported by the Coordinators of Scientific Research Projects of Konya Technical University (P.N.: 191013018). | en_US |
dc.language.iso | en | en_US |
dc.publisher | INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC | en_US |
dc.relation.ispartof | TRAITEMENT DU SIGNAL | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | automated diagnosis | en_US |
dc.subject | deep convolutional neural networks | en_US |
dc.subject | image classification | en_US |
dc.subject | spatial pyramid pooling | en_US |
dc.subject | tuberculosis | en_US |
dc.title | Spatial Pyramid Pooling in Deep Convolutional Networks for Automatic Tuberculosis Diagnosis | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.18280/ts.370620 | - |
dc.identifier.scopus | 2-s2.0-85099781761 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.identifier.volume | 37 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.startpage | 1075 | en_US |
dc.identifier.endpage | 1084 | en_US |
dc.identifier.wos | WOS:000605984500020 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57221681734 | - |
dc.authorscopusid | 56572779600 | - |
dc.authorscopusid | 57221683361 | - |
dc.identifier.scopusquality | Q3 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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File | Size | Format | |
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37.06_20.pdf | 1.62 MB | Adobe PDF | View/Open |
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