Advanced neural network solution for detection of lung pathology and foreign body on chest plain radiographs

Lilian Nitris, Evgenii Zhukov, Dmitry Blinov, Pavel Gavrilov, Ekaterina Blinova, Alina Lobishcheva, Ирина Григорьевна Камышанская

Результат исследований: Научные публикации в периодических изданияхстатьярецензирование

Аннотация

Objective: An approach was suggested to detect whether patient has any lung pathology or not. Materials and Methods: The approach was based on neural networks-aided analysis of chest X-ray frontal images. The neural network ensemble included 15 neural networks. Some of them were trained to analyze different parts of chest area, i.e, heart, diaphragm, lungs and related parts. And the other set of networks were trained to describe another meta-data of X-rays, such as patient position (laying or standing), quality of image, etc. The set of outputs of every model was aggregated with boosting model then. Result of model prediction was presented as probability of lung pathology on radiograph. Another 2 models were described in this article as parts of suggested approach. One of these model was trained to detect if there any foreign body within chest area on X-ray image or not. Another one was trained to classify which kind of foreign body was visualized (after first model gave positive prediction). To train models 9093 frontal X-ray images were used. Those images were labeled by group of radiologist. Results: The study showed that both foreign bodies detection and classification models demonstrated satisfactory results. The weak part of both models was precision for negative classes (those classes are “non-medical artefact” for one model and “foreign body is not visualized” for the other one). But it was not so critical for such kind of tasks. Conclusions: the model may be used to help a practitioner make decision whether a patient needs additional diagnostics or not.
Язык оригиналаанглийский
Страницы (с-по)57-66
Число страниц10
ЖурналQuantitative Imaging in Medicine and Surgery
Номер выпуска11(5)
СостояниеОпубликовано - 2019

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