Osman Güler1, Kemal Polat2, *
Kemal Polat
1 Tusaş Şehit Hakan Gülşen Vocational and Technical High School, Ankara 06890, Turkey
2 Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey
* Corresponding Author: Kemal Polat, Email: [email protected]
Pneumonia is one of the leading diseases of child mortality in the world. The fastest imaging method for detecting pneumonia in chest X-rays. Examining X-ray images is carried out by expert radiologists. It is important to develop computer-aided diagnosis systems due to the difficulty of the images examined. In this study, DenseNet121, DenseNet169, ResNet50, ResNet101, MobileNetV2, VGG16, Xception and InceptionV3 deep learning models were used to classify chest X-ray images. Experiments were done on the chest X-ray dataset of 5856 labeled images with the proposed models, and the results were compared. Transfer learning models have generally achieved high success rates in the problem of detecting pneumonia from chest X-ray images. The Xception model performed best with 96.16% validation and 95.73% test accuracy. It has been seen that transfer learning models are successful in classification problems.
Transfer Learning, Machine Learning, Deep Learning, Chest X-Ray, classification
Osman Güler, Kemal Polat (2022). Neural Network Learning of Context-Dependent Affordances. Journal of Artificial Intelligence and Systems, 4, 107–106. https://doi.org/10.33969/AIS.2022040107.
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