TitleAutomatic out-of-distribution detection methods for Improving the deep learning classification of pulmonary X-ray images
Publication typeJournal article
Author(s)A.A.Dovganich, A.V.Khvostikov, Ya.A.Pchelintsev, A.S.Krylov, Ding Yong, Farias Mylene C.Q.
Publication date2022
JournalJournal of Image and Graphics
AbstractIn this paper, we investigated the possibility of using medical differential criteria to determine the level of radiation in X-ray images of the lungs. We developed a new method for automatic determination and calculation the number of visible vertebrae in the pulmonary X-ray images and proposed a system of automatic out-of-distribution detection that can be used together with deep learning-based systems of pulmonary X-ray image analysis, in particular with the task of tuberculosis detection. The proposed method and system were evaluated using three X-ray lung datasets (Montgomery County chest X-ray dataset, Shenzhen chest X-ray dataset and Tuberculosis X-ray TBX11K dataset). We demonstrated that using the proposed system of out-of-distribution detection allows to enhance the tuberculosis classification results up to 1.3% using the same classification model. We also showed that the proposed system allows to automatically train a composite model which considers X-ray radiation level of the image, which is more effective compared to the traditional one-part model.

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