Abstract—In 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.
Index Terms—X-ray images, deep learning, quality assessment, out-of-distribution detection, detection of vertebrae
Cite: Andrey A. Dovganich, Alexander V. Khvostikov, Yakov A. Pchelintsev, Andrey A. Krylov, Yong Ding, and Mylene C. Q. Farias, "Automatic Out-of-Distribution Detection Methods for Improving the Deep Learning Classification of Pulmonary X-ray Images," Journal of Image and Graphics, Vol. 10, No. 2, pp. 56-63, June 2022.
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