dc.contributor.author | Baştürk, Alper | |
dc.contributor.author | Badem, Hasan | |
dc.contributor.author | Çalışkan, Abdullah | |
dc.contributor.author | Yüksel, Mehmet Emin | |
dc.date.accessioned | 12.07.201910:50:10 | |
dc.date.accessioned | 2019-07-12T22:02:48Z | |
dc.date.available | 12.07.201910:50:10 | |
dc.date.available | 2019-07-12T22:02:48Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Baştürk, A., Badem, H., Caliskan, A., Yüksel, M.E. (2019).
Application of deep neural networks for disease diagnosis through medical data sets. Smart Innovation, Systems and Technologies, 136, pp. 259-292.
https://doi.org/10.1007/978-3-030-11479-4_12 | |
dc.identifier.uri | https://doi.org/10.1007/978-3-030-11479-4_12 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/465 | |
dc.description.abstract | In this chapter, a novel classification methodology for medical disease diagnosis is proposed. The proposed classification operator comprises a stacked autoencoder network cascaded with a softmax layer. The classifier is trained by applying a special training approach, where each layer of the proposed classifier is trained individually and sequentially. The performance of the proposed classifier is compared with a number of representative classification methods from the literature. The experimental results on medical data sets show that the proposed classifier performs better than or at least competitive with classifiers used in this chapter. It is also seen that the proposed classifier can efficiently be used for the diagnosis of medical diseases provided that it is trained with a suitable data set with a sufficient number of medical features obtained from a sufficient number of patients. © 2019, Springer Nature Switzerland AG. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.isversionof | 10.1007/978-3-030-11479-4_12 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject.classification | Boltzmann Machine | Belief Networks | Generative | |
dc.subject.other | Classification (of information) | |
dc.subject.other | Deep neural networks | |
dc.subject.other | Auto encoders | |
dc.subject.other | Classification methodologies | |
dc.subject.other | Classification methods | |
dc.subject.other | Data set | |
dc.subject.other | Disease diagnosis | |
dc.subject.other | Medical data sets | |
dc.subject.other | Computer aided diagnosis | |
dc.title | Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets | en_US |
dc.type | bookPart | en_US |
dc.relation.journal | Smart Innovation, Systems and Technologies | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- Biyomedikal Mühendisliği Bölümü | en_US |
dc.identifier.volume | 136 | en_US |
dc.identifier.startpage | 259 | en_US |
dc.identifier.endpage | 292 | en_US |
dc.relation.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
dc.contributor.isteauthor | Çalışkan, Abdullah | |
dc.relation.index | Scopus | en_US |