TY  - JOUR
AU  - Iqbal, Sundas
AU  - Ali, Danish
AU  - Rafique, Sumaria
AU  - Bagga, Kajol
AU  - Rahman, Atif Ur
AU  - Khan, Salman
PY  - 2025
DA  - 2025/03/25
TI  - Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 1
SP  - 36
EP  - 47
DO  - 10.62762/TSCC.2025.378503
UR  - https://www.icck.org/article/abs/TSCC.2025.378503
KW  - knee osteoarthritis
KW  - biomedical sensing
KW  - transfer learning
KW  - convolutional neural networks
KW  - deep learning
AB  - Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically sense and classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model, establishing X-ray imaging as a biomedical sensing modality for automated disease grading. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99% training accuracy and 80% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning for 20 epochs, reached 99.8% training accuracy and 91% testing accuracy. Additionally, a web app was developed using Streamlit, enabling users to upload knee X-ray images and receive real-time predictions of OA severity. The results demonstrate the effectiveness of CNN and transfer learning with VGG16 in knee OA classification, offering a promising tool for early diagnosis in clinical settings.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Iqbal2025Comparativ,
  author = {Sundas Iqbal and Danish Ali and Sumaria Rafique and Kajol Bagga and Atif Ur Rahman and Salman Khan},
  title = {Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {1},
  pages = {36-47},
  doi = {10.62762/TSCC.2025.378503},
  url = {https://www.icck.org/article/abs/TSCC.2025.378503},
  abstract = {Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically sense and classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model, establishing X-ray imaging as a biomedical sensing modality for automated disease grading. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99\% training accuracy and 80\% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning for 20 epochs, reached 99.8\% training accuracy and 91\% testing accuracy. Additionally, a web app was developed using Streamlit, enabling users to upload knee X-ray images and receive real-time predictions of OA severity. The results demonstrate the effectiveness of CNN and transfer learning with VGG16 in knee OA classification, offering a promising tool for early diagnosis in clinical settings.},
  keywords = {knee osteoarthritis, biomedical sensing, transfer learning, convolutional neural networks, deep learning},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}