SYSTEMATIZATION OF ANOMALY SEARCH IN X-RAY PICTURES
Ключові слова:deep machine learning, convolutional neural network, pattern recognition methods, image visualization, roentgenogram, learning algorithm.
The algorithms of pathological and structural structures using neural networks allow accelerating the process of diagnosing anomalies, reducing the number of errors and repeated polls of users. The article discusses methods of machine systematization and recognition of X-ray
images (CXR), as well as the problems of improving artificial neural networks, which are used to enhance the properties of the systematization of X-ray syndromes. Since it is quite enough to
implement a certain algorithm to detect a disease, neural networks are ideal for recognizing diseases using scanning. After analyzing research and publications on this topic, the main tasks for
modeling the system were formed. The architectures of neural networks were also classified, indicating their disadvantages and advantages. It was revealed that modern methods of detecting
anomalies in CXR have some difficulties, such as the missing number of training information, image typing, and preliminary segmentation of the training set. Deterministic specific methods for solving the problems of neural networks in data analysis. For implementation, it is proposed to
apply deep machine learning methods, based on convolutional neural networks, using preliminary segmentation of a training sample with back propagation of error and gradient descent c and using
transfer learning to systematize diseases on medical images. To solve the tasks, innovative IT technologies were selected. As a result, a certain architecture of the intelligent system was
implemented, which allows us to detect anomalies in radiographs, which allow us to create effective structures of neural networks and increase the accuracy of recognition of pathological structures in
Travers Ch. Opportunities and obstacles to a deep study of biology and medicine / Ch.
Travers. - Royal Society Interface Bulletin - 2018. - 141p.
Rabich G. Deep convolutional neural networks for the detection of chest diseases / G.
Weinhoven R.G. Rapid object detection training using stochastic gradient output:
International Conference on Image Presence (ICPR) / R.G. Weinhoven - Tsukuba, Japan, 2010 -
Pattrapisetwong P. Automatic lung segmentation in chest X-ray using a shadow filter:
proceedings of the IEEE / P. Pattrapisetwong Conference. - Manchester, United Kingdom, 2016.
Goodfellow I., Bengio Y., Courville A. Deep Learning. –– MIT Press, 2016. –– Access
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for
Chest X-Ray Analysis of Lung Cancer / Yu. Gordienko, P. Gang, J. Hui et al. // ArXiv. –– 2017 ––
Access mode: https: // arxiv.org/abs/1712.07632.