Designing CNN Neural Network Model for Detecting Fractures of Lower Extremities by X-ray Images

Скорочена інформація про документ
dc.contributor.authorМазурець, Олександр Вікторович
dc.contributor.authorKharysh, I.
dc.contributor.authorSobko, O.
dc.contributor.authorMazurets, O.
dc.date.accessioned2024-10-30T10:48:27Z
dc.date.available2024-10-30T10:48:27Z
dc.date.issued2024
dc.description.abstractThe problem of using CNN neural network model for detecting fractures of lower extremities by X-ray images was investigated. In particular, the Dense Convolutional Network architecture is used, which is one of the advanced convolutional neural networks, which is well suited for the task of identifying bone fractures in X-ray images due to its efficiency and ability to store information at all levels of the network. It differs from traditional CNNs in that each layer is connected to all previous layers, which allows storing information and facilitates the transfer of gradients during training. Regarding the advantages of DenseNet over other networks, it has fewer parameters compared to others, which reduces the need for computing resources. Also improved feature retention, where the model better remembers and uses features from earlier layers, improving its ability to recognize small details important for fracture detection.
dc.identifier.citationKharysh I., Sobko O., Mazurets O. Designing CNN Neural Network Model for Detecting Fractures of Lower Extremities by X-ray Images. The Impact of Scientific Research on the Development of the Modern World. Proceedings of the XLІV International scientific and practical conference. October 23-25, 2024. Dubrovnik, Croatia. 2024. Pp. 91-96
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/16947
dc.language.isoen
dc.titleDesigning CNN Neural Network Model for Detecting Fractures of Lower Extremities by X-ray Images
dc.typeСтаття
Файли
Контейнер файлів
Зараз показуємо 1 - 1 з 1
Ескіз
Назва:
The_impact_of_scientific_research_on_the_development_of_the_modern_world_October_23_25_2024_Dubrovnik_Croatia-91-96.pdf
Розмір:
154.45 KB
Формат:
Adobe Portable Document Format
Завантажити
Ліцензійна угода
Зараз показуємо 1 - 1 з 1
Назва:
license.txt
Розмір:
4.26 KB
Формат:
Item-specific license agreed upon to submission
Опис:
Завантажити
Зібрання
Кафедра комп’ютерних наук