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Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosisстатья
- Авторы: Pchelintsev Ya A., Khvostikov A.V., Krylov A.S., Parolina L.E., Nikoforova N.A., Shepeleva L.P., Prokop’ev E.S., Farias M., Yong Ding
- Журнал: Computational Mathematics and Modeling
- Том: 33
- Номер: 2
- Год издания: 2023
- Издательство: Consultants Bureau
- Местоположение издательства: United States
- Первая страница: 230
- Последняя страница: 243
- DOI: 10.1007/s10598-023-09568-3
- Аннотация: We consider the automatic hardness determination of a chest X-ray image and the effect of pre-filtering of the training and validation samples on the performance of the classification algorithm of tuberculosis diagnosis from chest X-rays. Convolutional neural networks are used in automatic hardness determination and tuberculosis diagnosis. The results of the present study are compared with those from different datasets, including datasets pruned by image hardness criteria. Keywords: chest X-ray images, tuberculosis diagnosis, quality control, convolutional neural networks, radiograph hardness.
- Добавил в систему: Хвостиков Александр Владимирович