Processing and Analyzing Images based on a Neural Network

Zhurakovskyi, B. та Poltorak, V. та Toliupa, S. та Pliushch, O. та Platonenko, Artem (2024) Processing and Analyzing Images based on a Neural Network Cybersecurity Providing in Information and Telecommunication Systems 2024, 3654. с. 125-136. ISSN 1613-0073

Текст B_Zhurakovskyi_V_ Poltorak_S_Toliupa_O_Pliushch_A_Platonenko_CPITS_3654_2024.pdf Download (922kB)

Анотація

Medical image processing technologies allow for automating and improving diagnostic and analysis processes, providing doctors with more accurate and faster results. The use of artificial intelligence, deep learning, and computer vision allows for the creation of efficient and automated systems that can detect pathologies, classify images, and provide valuable decision support to doctors. The description and preliminary processing of the data set, which is a key stage for the preparation of system input data, has been performed. Models for training are also developed, including the selection and tuning of neural network architectures. The introduction of a new method for training a neural network turned out to be very successful. This approach significantly improved the training quality of the model, helping to increase the accuracy and ability of image classification. The application of this method significantly improved the efficiency and reliability of the X-ray image recognition system. The research results indicate that the new learning method, based on the combination of Adam and SGD methods, raised the accuracy of image recognition to the level of 95–97% while increasing the training time by only 1–2%. The developed system can be considered as an initial version that paves the way for further improvement. It was determined that the main driving factor for improving the system is the developed neural network training method.

Тип елементу :	Стаття
Ключові слова:	Image recognition; neural network; machine learning; system; classification; model; model training; dataset; accuracy; training; efficiency
Типологія:	Статті у базах даних > Scopus
Підрозділи:	Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки ім. професора Володимира Бурячка
Користувач, що депонує:	Павло Миколайович Складанний
Дата внесення:	09 Квіт 2024 07:29
Останні зміни:	09 Квіт 2024 07:29
URI:	https://elibrary.kubg.edu.ua/id/eprint/48596

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