Publication Type : Conference Paper
Publisher : IEEE
Source : 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)
Url : https://doi.org/10.1109/accai58221.2023.10200102
Campus : Amaravati
School : School of Computing
Year : 2023
Abstract : Osteoarthritis (OA) disease most caused in elderly people which causes muscle and skeleton system damage. [1] Early prediction of this disease helps to reduce its severity. This paper presents a decent literature review of different prediction models related to OA. Due to the availability of different technical algorithms, the image-based prediction to detect the presence of osteoarthritis is carried out from a dataset available on Kaggle. This work was carried out with different deep learning models like Efficient-V2L, MobileNet, VGG16, and GoogleNet. The findings justify that the Efficient-V2L model has obtained a good accuracy with 93.96% and performs well to predict OA when compared with other existing models.
Cite this Research Publication : Nagendra Panini Challa, Beebi Naseeba, Gudigntla Vyshnavi, Thanneeru Priyanka, Nagaraju Jajam, Kamepalli SL Prasanna, Osteoarthritis Disease Detection using Efficient Hyper-Tuning Parameters, 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, 2023, https://doi.org/10.1109/accai58221.2023.10200102
