Deep and Machine Learning towards Pneumonia and Asthma Detection
| dc.authorscopusid | 57027754300 | |
| dc.authorscopusid | 6602826664 | |
| dc.contributor.author | Yahyaoui, A. | |
| dc.contributor.author | Yumusak, N. | |
| dc.date.accessioned | 2022-03-04T19:12:28Z | |
| dc.date.available | 2022-03-04T19:12:28Z | |
| dc.date.issued | 2021 | |
| dc.department | İZÜ | en_US |
| dc.description | 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 -- 29 September 2021 through 30 September 2021 -- | en_US |
| dc.description.abstract | Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method. © 2021 IEEE. | en_US |
| dc.identifier.doi | 10.1109/3ICT53449.2021.9581963 | |
| dc.identifier.endpage | 497 | en_US |
| dc.identifier.isbn | 9781665440325 | |
| dc.identifier.scopus | 2-s2.0-85119402597 | en_US |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 494 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/3ICT53449.2021.9581963 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12436/3213 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Asthma | en_US |
| dc.subject | chest diseases | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | Deep Neural Network | en_US |
| dc.subject | K nearest neighbors | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | Pneumonia | en_US |
| dc.subject | Biological organs | en_US |
| dc.subject | Motion compensation | en_US |
| dc.subject | Nearest neighbor search | en_US |
| dc.subject | Pulmonary diseases | en_US |
| dc.subject | Asthma | en_US |
| dc.subject | Chest disease | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | High-dimensional | en_US |
| dc.subject | Higher-dimensional | en_US |
| dc.subject | K near neighbor | en_US |
| dc.subject | Machine-learning | en_US |
| dc.subject | Medical fields | en_US |
| dc.subject | Nearest-neighbour | en_US |
| dc.subject | Pneumonia | en_US |
| dc.subject | Deep neural networks | en_US |
| dc.title | Deep and Machine Learning towards Pneumonia and Asthma Detection | en_US |
| dc.type | Conference Object | |
| dspace.entity.type | Publication |









