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10.14489/vkit.2019.06.pp.032-038

DOI: 10.14489/vkit.2019.06.pp.032-038

Мустафаев А. Г.
ИСПОЛЬЗОВАНИЕ НЕЙРОСЕТЕВЫХ ТЕХНОЛОГИЙ В ЗАДАЧАХ МЕДИЦИНСКОЙ ДИАГНОСТИКИ
(с. 32-38)

Аннотация. Рассмотрены основные проблемы в подготовке входных данных при обучении нейронной сети – выбор существенных признаков, нормализация и переобучение. Представлен общий подход использования искусственных нейронных сетей в диагностике на основе общедоступных наборов данных, которые могут служить в качестве исходных для нейронных сетей. Даны описания примеров практического использования.

Ключевые слова:  нейронная сеть; машинное обучение; медицинская диагностика; обработка данных.

 

Mustafaev A. G.
NEURAL NETWORK TECHNOLOGY IN TASKS OF MEDICAL DIAGNOSTICS
(pp. 32-38)

Abstract. Medical diagnosis is an important task that must be performed as accurately and efficiently as possible. Diagnosis is a special case of the classification of events, and the greatest value is the classification of those events that have never met a doctor before. Computerized diagnostic methods are promising tool that allows doctors to clarify an intricate situation in case of inaccuracies and uncertainties. Currently, clinical specialists have access to a vast amount of information, ranging from details of clinical symptoms to various types of biochemical data and images obtained by various methods. To avoid a misdiagnosis associated with the “human factor”, machine learning methods are used. Machine learning and neural networks provide an opportunity to improve the quality in the health service sector, focusing on the prevention and early detection of diseases. Quality machine learning is only possible with a reliable source of consensus data sets. In this paper, the main problems in the preparation of input data in the training of a neural network are considered: the choice of essential features, normalization and overfitting. A general approach to the use of artificial neural networks in diagnostics, based on publicly available data sets that can serve as the source for neural networks, is presented. The author developed a system for early diagnosis of patients with diabetes mellitus using artificial neural networks, based on a multilayer perceptron trained by the back propagation algorithm.

Keywords: Neural network; Machine learning; Medical diagnostics; Data processing.

Рус

А. Г. Мустафаев (Дагестанский государственный университет народного хозяйства, Махачкала, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Eng

A. G. Mustafaev (Dagestan State University of National Economy, Makhachkala, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

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Eng

1. Roth H. R. et al. (2016). Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation. IEEE Transactions on Medical Imaging, 35(5), pp. 1170-1181.
2. Al-Sammarraie N. A., Al-Mayali Y. M. H., Baker El-Ebiary Y. A. (2018). Classification and Diagnosis Using Back Propagation Artificial Neural Networks (ANN). 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE). Shah Alam. Malaysia, pp. 277-282.
3. Babu M. S. P., Katta S. (2015). Artificial Immune Recognition Systems in Medical Diagnosis, pp. 1082 – 1087. 2015 16th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing, pp. 1082-1087.
4. Gusev A. V. (2017). Prospects for neural networks and deep machine learning in creating healthcare solutions. Vrach i informatsionnye tekhnologii, (3), pp. 92-105. [in Russian language]
5. Farrugia A. et al. (2013). Medical Diagnosis: Are Artificial Intelligence Systems Able to Diagnose the Underlying Causes of Specific Headaches? 2013 Sixth International Conference on Developments in eSystems Engineering. Abu Dhabi. United Arab Emirates, pp. 376-382. doi: 10.1109/DeSE.2013.72
6. Shin H. et al. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, Vol. 35, (5), pp. 1285-1298.
7. Yang H. et al. (2014). Healthcare Intelligence: Turning Data into Knowledge. IEEE Intelligent Systems, 29(3), pp. 54-68.
8. Yao C. et al. (2016). A Convolutional Neural Network Model for Online Medical Guidance. IEEE Access, Vol. 4, pp. 4094 – 4103.
9. Ravì D. et al. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), pp. 4-21. doi: 10.1109/ JBHI.2016.2636665
10. Tajbakhsh N. et al. (2016). Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? IEEE Transactions on Medical Imaging, Vol. 35, (5), pp. 1299-1312.
11. Bilbao I., Bilbao J. (2017). Overfitting Problem and the Over-Training in the Era of Data: Particularly for Artificial Neural Networks. Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 173-177.
12. Xu C. et al. (2016). Multi-Loss Regularized Deep Neural Network. IEEE Transactions on Circuits and Systems for Video Technology, 26(12), pp. 2273-2283.
13. Afshar S. et al. (2011). Recognition and Prediction of Leukemia with Artificial Neural Network (ANN). Medical Journal of Islamic Republic of Iran, 25(1), pp. 35-39.
14. Lapta S. I., Solov'eva O. I. (2017). Artificial neural network for early diagnosis of type 2 diabetes. Sistemi obrobki іnformatsії, (1), pp. 147-151. Available at: http://nbuv.gov.ua/UJRN/soi_2017_1_29 (Accessed: 02.05.2019)
15. Al-Shayea Q. K. (2011). Artificial Neural Networks in Medical Diagnosis. International Journal of Computer Science Issues, 8(2), pp. 150-154.

Рус

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