Diagnosis of Pneumonia from Chest X-Ray Images using Deep Learning
| dc.contributor.author | Ayan, Enes | |
| dc.contributor.author | Unver, Halil Murat | |
| dc.date.accessioned | 2020-06-25T18:34:28Z | |
| dc.date.available | 2020-06-25T18:34:28Z | |
| dc.date.issued | 2019 | |
| dc.department | Kırıkkale Üniversitesi | |
| dc.description | International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEY | |
| dc.description.abstract | Pneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process. Generally, the disease can be diagnosed from chest X-ray images by an expert radiologist. The diagnoses can be subjective for some reasons such as the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Therefore, computer-aided diagnosis systems are needed to guide the clinicians. In this study, we used two well-known convolutional neural network models Xception and Vgg16 for diagnosing of pneumonia. We used transfer learning and fine-tuning in our training stage. The test results showed that Vgg16 network exceed Xception network at the accuracy with 0.87%, 0.82% respectively. However, the Xception network achieved a more successful result in detecting pneumonia cases. As a result, we realized that every network has own special capabilities on the same dataset. | en_US |
| dc.description.sponsorship | IEEE Turkey Sect, IEEE EMB, Erasmus+, Europass | en_US |
| dc.description.sponsorship | Research Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University [:2018/40] | en_US |
| dc.description.sponsorship | This work was supported by Research Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University. Project Number:2018/40. | en_US |
| dc.identifier.citation | closedAccess | en_US |
| dc.identifier.isbn | 978-1-7281-1013-4 | |
| dc.identifier.scopus | 2-s2.0-85068539603 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12587/7918 | |
| dc.identifier.wos | WOS:000491430200005 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2019 Scientific Meeting On Electrical-Electronics & Biomedical Engineering And Computer Science (Ebbt) | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Pneumonia | en_US |
| dc.subject | transfer learning | en_US |
| dc.subject | Xception | en_US |
| dc.subject | Vgg16 | en_US |
| dc.subject | deep learning | en_US |
| dc.title | Diagnosis of Pneumonia from Chest X-Ray Images using Deep Learning | en_US |
| dc.type | Conference Object |
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