Oltu, BurcuGuney, SeldaDengiz, BernaAgildere, Muhtesem2022-08-092022-08-092021978-1-6654-2933-7http://hdl.handle.net/11727/7282Tuberculosis (TB) is a dreadfully contagious and life-threatening disease if left untreated. Therefore, early and accurate diagnosis is critical for treatment. Today, invasive, expensive, or time-consuming tests are performed for diagnosis. Unfortunately, accurate TB diagnosis is still a major challenge. In the proposed study, a decision support system that can automatically separate normal and TB chest X-ray (CXR) images is presented for objective and accurate diagnosis. In the presented methodology, first various data augmentation methods were applied to the data set, then pre-trained networks (VGG16, MobileNet), were employed as feature extractors from augmented CXR's. Afterward, the extracted features for all images were fed into a support vector machine classifier. In training process, 5-fold cross-validation was applied. As a result of this classification, it was concluded that TB can be diagnosed with an accuracy of 96,6% and an area under the ROC curve (AUC) of 0,99.enginfo:eu-repo/semantics/closedAccesstuberculosisdeep learningSVMtransfer learningpre-trained networksconferenceObject92950007016046000202-s2.0-85115443787