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Optimizing KOA diagnosis and classification using inception CNN model
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DOI：10.1007/s42452-026-09097-9.png)
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Abstract
En 中文
Knee Osteoarthritis (KOA) is an age-related progressive degeneration of the joint, characterized by the cartilage loss, joint space narrowing and bone spurs, that leads to pain, stiffness and impaired mobility of the knee joints. Proper diagnosis and the severity of knee radiographs can be treated effectively on the basis of sound treatment planning but manual grading of knee radiographs based on the Kellgren-Lawrence (KL) scale is time-consuming and prone to inter-observer variation. In order to cope with this difficulty, this paper suggests a deep learning-based mechanism of automated KOA recognition and severity classification founded on knee X-ray photographs. The data was acquired in the Osteoarthritis Initiative (OAI) database and Kaggle repositories. Before model training, images underwent image processing with the use of resizing, normalization, and data augmentation to enhance generalization. The InceptionV3 convolutional neural network was used to obtain discriminative radiographic features with the help of transfer learning. The proposed framework carries out three classification tasks, including binary classification to identify whether KOA has occurred, three-classification to determine the severity of the disease, and four-classification according to the KL grading levels. The experimental findings indicate that the model has an accuracy of 82.4, 88.1 and 62.3%. The efficiency of the model in binary, three-class and four-class classification problems was tested by using precision, recall and F1-score as evaluation metrics. These results suggest that models based on deep learning can be used to facilitate automated KOA disease detection and severity evaluation, which can potentially be a decision-support instrument in clinical diagnosis.
Keywords:
KOA
Images
Artificial intelligence
Machine learning
classification
OAI
Inception–v3
Deep learning
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