|
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.
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
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
1. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation / H. R. Roth et al. // IEEE Transactions on Medical Imaging. 2016. V. 35, No. 5. P. 1170 – 1181.
2. Al-Sammarraie N. A., Al-Mayali Y. M. H., Baker El-Ebiary Y. A. Classification and Diagnosis Using Back Propagation Artificial Neural Networks (ANN) // 2018 Intern. Conf. on Smart Computing and Electronic Enterprise (ICSCEE). 11–12 Jule 2018, Shah Alam, Malaysia. 2018. P. 277 – 282.
3. Babu M. S. P., Katta S. Artificial Immune Recognition Systems in Medical Diagnosis // 2015 6th IEEE Intern. Conf. on Software Engineering and Service Science (ICSESS). Beijing. 2015. P. 1082 – 1087.
4. Гусев А. В. Перспективы нейронных сетей и глубокого машинного обучения в создании решений для здравоохранения // Врач и информационные технологии. 2017. № 3. С. 92 – 105.
5. Medical Diagnosis: Are Artificial Intelligence Systems Able to Diagnose the Underlying Causes of Specific Headaches? / A. Farrugia et al. // 2013 Sixth Intern. Conf. on Developments in eSystems Engineering. 13 – 16 Dec. 2013, Abu Dhabi, United Arab Emirates. 2013. P. 376 – 382. doi: 10.1109/DeSE.2013.72
6. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning / H. Shin et al. // IEEE Transactions on Medical Imaging. 2016. V. 35, No. 5. P. 1285 – 1298.
7. Healthcare Intelligence: Turning Data into Knowledge / H. Yang et al. // IEEE Intelligent Systems. 2014. V. 29, No. 3. P. 54 – 68.
8. A Convolutional Neural Network Model for Online Medical Guidance / C. Yao et al. // IEEE Access. 2016. V. 4. P. 4094 – 4103.
9. Deep Learning for Health Informatics / D. Ravì et al. // IEEE Journal of Biomedical and Health Informatics. 2017. V. 21, No. 1. P. 4 – 21. doi: 10.1109/ JBHI.2016.2636665
10. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? / N. Tajbakhsh et al. // IEEE Transactions on Medical Imaging. 2016. V. 35, No. 5. P. 1299 – 1312.
11. Bilbao I., Bilbao J. Overfitting Problem and the Over-Training in the Era of Data: Particularly for Artificial Neural Networks // Eighth Intern. Conf. on Intelligent Computing and Information Systems (ICICIS). 2017. P. 173 – 177.
12. Multi-Loss Regularized Deep Neural Network / C. Xu et al. // IEEE Transactions on Circuits and Systems for Video Technology. 2016. V. 26, No. 12. P. 2273 – 2283.
13. Recognition and Prediction of Leukemia with Artificial Neural Network (ANN) / S. Afshar et al. // Medical Journal of Islamic Republic of Iran. 2011. V. 25, No. 1. P. 35 – 39.
14. Лапта С. И., Соловьева О. И. Искусственная нейронная сеть для ранней диагностики сахарного диабета 2 типа [Электронный ресурс] // Системи обробки інформації. 2017. Вип. 1. С. 147 – 151. URL: http://nbuv.gov.ua/UJRN/soi_2017_1_29 (дата обращения: 02.05.2019).
15. Al-Shayea Q. K. Artificial Neural Networks in Medical Diagnosis // Intern. Journal of Computer Science Issues. 2011. V. 8, Is. 2. P. 150 – 154.
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.
Статью можно приобрести в электронном виде (PDF формат).
Стоимость статьи 350 руб. (в том числе НДС 18%). После оформления заказа, в течение нескольких дней, на указанный вами e-mail придут счет и квитанция для оплаты в банке.
После поступления денег на счет издательства, вам будет выслан электронный вариант статьи.
Для заказа скопируйте doi статьи:
10.14489/vkit.2019.06.pp.032-038
и заполните форму
Отправляя форму вы даете согласие на обработку персональных данных.
.
This article is available in electronic format (PDF).
The cost of a single article is 350 rubles. (including VAT 18%). After you place an order within a few days, you will receive following documents to your specified e-mail: account on payment and receipt to pay in the bank.
After depositing your payment on our bank account we send you file of the article by e-mail.
To order articles please copy the article doi:
10.14489/vkit.2019.06.pp.032-038
and fill out the form
.
|
Archive
Разработка концепции и создание сайта - ООО «Издательский дом «СПЕКТР»