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dc.contributor.authorAKGÜNDOĞDU, Abdurrahim
dc.date.accessioned2021-03-02T16:31:15Z
dc.date.available2021-03-02T16:31:15Z
dc.identifier.citationAKGÜNDOĞDU A., "Detection of pneumonia in chestX-rayimages by using2Ddiscrete wavelet feature extraction with random forest", INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2020
dc.identifier.issn0899-9457
dc.identifier.othervv_1032021
dc.identifier.otherav_638d66d8-6354-4029-847d-af713058f0ce
dc.identifier.urihttp://hdl.handle.net/20.500.12627/3063
dc.identifier.urihttps://doi.org/10.1002/ima.22501
dc.description.abstractPneumonia is one of the most common and fatal diseases in the world. Early diagnosis and treatment are important factors in reducing mortality caused by the aforementioned disease. One of the most important and common techniques to diagnose pneumonia disease is the X-ray images. By evaluating these images, various machine-learning methods are used for accuracy in diagnosis. The presented study in this article utilizes machine-learning techniques to evaluate these X-ray images. The diagnosis of pediatric pneumonia is classified with a proposed machine learning method by using the chest X-ray images. The proposed system firstly utilizes a two-dimensional discrete wavelet transform to extract features from images. The features obtained from the wavelet method are labeled as normal and pneumonia and applied to the classifier for classification. Besides, Random Forest algorithm is used for the classification technique of 5856 X-ray images. A 10-fold cross-validation method is used to evaluate the success of the proposed method and to ensure that the system avoided overfitting. By using various machine learning algorithms, simulation results reveal that the Random Forest method is proposed and it gives successful results. Results also show that, at the end of the training and validation process, the proposed method achieves higher success with an accuracy of 97.11%.
dc.language.isoeng
dc.subjectTemel Bilimler (SCI)
dc.subjectGÖRÜNTÜLEME BİLİMİ VE FOTOĞRAF TEKNOLOJİSİ
dc.subjectKlinik Tıp
dc.subjectKlinik Tıp (MED)
dc.subjectTıp
dc.subjectSağlık Bilimleri
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectSinyal İşleme
dc.subjectElektromanyetizma, Akustik, Isı Transferi, Klasik Mekanik ve Akışkanlar Dinamiği
dc.subjectOptik
dc.subjectOPTİK
dc.subjectMühendislik ve Teknoloji
dc.subjectTemel Bilimler
dc.subjectFizik
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.titleDetection of pneumonia in chestX-rayimages by using2Ddiscrete wavelet feature extraction with random forest
dc.typeMakale
dc.relation.journalINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
dc.contributor.departmentİstanbul Üniversitesi-Cerrahpaşa , Mühendislik Fakültesi , Elektrik Elektronik Mühendisliği Bölümü
dc.contributor.firstauthorID2287458


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