Detection of early pneumonia on individual CT scans with dilated convolutions

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dc.contributor.authorKrak, Iurii
dc.contributor.authorBarmak, Olexander
dc.contributor.authorRadiuk, Pavlo
dc.date.accessioned2021-12-14T15:00:29Z
dc.date.available2021-12-14T15:00:29Z
dc.date.issued2021-04-23
dc.descriptionhttp://ceur-ws.org/Vol-2853/paper20.pdfuk_UA
dc.description.abstractOver the past decades, pneumonia has been considered one of the most dangerous diseases, leading to severe consequences in a short time. Without proper and timely treatment, pneumonia can lead to fatal consequences. Thus, early diagnosis and detection of this lung disease are crucial in successful treatment and constant monitoring. Indeed, there is a high demand for the development of medical image technologies for disease identification. In this paper, we propose a novel information technology for robust feature identification and early detection of pneumonia on computer tomography scans. We also propose a new modified convolutional neural network as a core feature extractor. An effective dilated convolution operation with different rates, combining features of various receptive fields, was utilized to detect and analyze visual deviations in targeted images. Due to applying the dilated convolutions, the network avoids significant losses of objects' spatial information while providing low computational losses. The investigated model classifies computed tomography images with a validation accuracy of up to 96.12%. Overall, our approach requires much fewer computing resources, proving its effectiveness for solving practical problems on available computing devices.uk_UA
dc.identifier.citationKrak Iu., Barmak O., Radiuk P. Detection of early pneumonia on individual CT scans with dilated convolutions // 2020. CEUR-Workshop Proceedings. 2021. Vol. 2853. P. 214-227.uk_UA
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/11059
dc.language.isoenuk_UA
dc.publisherCEUR-WSuk_UA
dc.subjectPneumonia detectionuk_UA
dc.subjectcomputer tomographyuk_UA
dc.subjectfeature extractionuk_UA
dc.subjectdeep learninguk_UA
dc.subjectconvolutional neural networkuk_UA
dc.subjectdilated convolutionuk_UA
dc.subjectindividual approachuk_UA
dc.titleDetection of early pneumonia on individual CT scans with dilated convolutionsuk_UA
dc.typeСтаттяuk_UA
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