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Automatic Out-of-Distribution Detection Methods for Improving the Deep Learning Classification of Pulmonary X-ray Imagesстатья
- Авторы: Dovganich Andrey A., Khvostikov Alexander V., Pchelintsev Yakov A., Krylov Andrey S., Ding Yong, Farias Mylene C.Q.
- Журнал: Journal of Image and Graphics
- Том: 10
- Номер: 2
- Год издания: 2022
- Издательство: University of Portsmouth
- Местоположение издательства: UK
- Первая страница: 230
- Последняя страница: 243
- DOI: 10.18178/joig.10.2.56-63
- Аннотация: 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
- Добавил в систему: Хвостиков Александр Владимирович