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NEURAL NETWORK INFORMATION TECHNOLOGY FOR RECOGNITION AND CLASSIFICATION OF IMAGE PRESENTATIONS OF RENAL CELL CARCINOMA COMPLICATED CHRONIK KIDNEY DISEASES FOR SELECTION OF METHOD METHOD = НЕЙРОМЕРЕЖЕВА ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ РОЗПІЗНАВАННЯ ТА КЛАСИФІКАЦІЇ ОБРАЗНИХ ПРЕДСТАВЛЕНЬ НИРКОВО-КЛІТИННОГО РАКУ УСКЛАДНЕНОГО ХРОНІЧНОЮ ХВОРОБОЮ НИРОК ДЛЯ ВИБОРУ МЕТОДУ ЛІКУВАННЯ

S. M. Pasichnyk, C. B. Shatnyy, A. I. Gozhenko
2020 Zenodo  

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Pasichnyk S. M., Shatnyy C. B., Gozhenko A. I. NEURAL NETWORK INFORMATION TECHNOLOGY FOR RECOGNITION AND CLASSIFICATION OF IMAGE PRESENTATIONS OF RENAL CELL CARCINOMA COMPLICATED CHRONIK KIDNEY DISEASES FOR SELECTION OF METHOD METHOD = НЕЙРОМЕРЕЖЕВА ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ РОЗПІЗНАВАННЯ ТА КЛАСИФІКАЦІЇ ОБРАЗНИХ ПРЕДСТАВЛЕНЬ НИРКОВО-КЛІТИННОГО РАКУ УСКЛАДНЕНОГО ХРОНІЧНОЮ ХВОРОБОЮ НИРОК ДЛЯ ВИБОРУ МЕТОДУ ЛІКУВАННЯ. Актуальні проблеми транспортної медицини / Actual problems of transport medicine /
more » ... 2020;4(62):33-44. ISSN 1818-9385 DOI http://dx.doi.org/10.5281/zenodo.4396147 http://aptm.org.ua УДК 616–006.6–08 DOI: http://dx.doi.org/10.5281/zenodo.4396147 NEURAL NETWORK INFORMATION TECHNOLOGY FOR RECOGNITION AND CLASSIFICATION OF IMAGE PRESENTATIONS OF RENAL CELL CARCINOMA COMPLICATED CHRONIK KIDNEY DISEASES FOR SELECTION OF METHOD METHOD Pasichnyk S.M.1, Shatnyy C.B.2 Gozhenko A.I.3. 1Danylo Halytsky National Medical University of Lviv, Lviv 2National University of Water Management and Nature Management, Rivne 3Ukrainian Research Institute of Transport Medicine of the Ministry of Health of Ukraine, Odessa Summary The information technology of recognition and classification of imaging representations of RCC complicated CKD with use of a neural network is offered. Approaches to architecture design, teaching methods, data preparation for training, training and neural network testing are described. The structural-functional scheme of the neural network is developed, which consists of the input, hidden and output layer, each individual neuron is described by the corresponding activation function with the selected weights. The expediency of using the number of neurons, their type and architecture for the task of recognition and classification of image representations of oncological phenomena of the organism is shown. Data of patients with RCC of complicated CKD, research department of reconstructive and plastic oncourology of NIR, urological department of "Lviv regional hospital", urology department of Lviv urological reg [...]
doi:10.5281/zenodo.4396147 fatcat:bozuncd4ojffvmudzuynotm2i4
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S. M. Pasichnyk, C. B. Shatnyy, A. I. Gozhenko. "NEURAL NETWORK INFORMATION TECHNOLOGY FOR RECOGNITION AND CLASSIFICATION OF IMAGE PRESENTATIONS OF RENAL CELL CARCINOMA COMPLICATED CHRONIK KIDNEY DISEASES FOR SELECTION OF METHOD METHOD = НЕЙРОМЕРЕЖЕВА ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ РОЗПІЗНАВАННЯ ТА КЛАСИФІКАЦІЇ ОБРАЗНИХ ПРЕДСТАВЛЕНЬ НИРКОВО-КЛІТИННОГО РАКУ УСКЛАДНЕНОГО ХРОНІЧНОЮ ХВОРОБОЮ НИРОК ДЛЯ ВИБОРУ МЕТОДУ ЛІКУВАННЯ." Zenodo (2020)