Transfer Learning Based Deep Learning Approach for Knee Osteoarthritis Grading Using Modified XceptionNet Architecture

Summary

In order to enhance knee osteoarthritis identification from X-rays, this study suggests OsteoXceptionNet, a deep learning model that uses modified XceptionNet with transfer learning. This model improves feature extraction, lowers manual interpretation errors, and allows for more precise, automated classification.

Abstract

Knee osteoarthritis (KOA) affects millions of individuals worldwide and has no known curative treatment, making it a serious global health concern. The management of its development depends on early discovery, and X-ray imaging is a fundamental diagnostic technique. However, due to variations in radiologists' levels of experience, manual X-ray interpretation increases variability and possible inaccuracies. Recent advances in machine learning and deep learning techniques have sparked the creation of automated systems for the radiological identification of osteoarthritis in the knee. However, for early-stage detection, obtaining greater prediction accuracy is still crucial. By utilizing the insights gathered from a bigger dataset, models trained on smaller, domain-specific datasets perform better through the use of transfer learning. Due to its depth and effectiveness, XceptionNet is especially well-suited for jobs involving the interpretation of medical images. In contrast to previous research, this method efficiently addresses dataset imbalance by using class balancing approaches, integrating a customized preprocessing pipeline, and adding customized architectural improvements to XceptionNet, which improves early-stage KOA identification. With the use of these state-of-the-art methods, The suggested approach shows potential in correctly identifying osteoarthritis from radiographic images of the knee, attaining 97% prediction accuracy, 97.8% precision, 97.6% recall, and 97.6% F1-measure. Additionally, the generated model showed 95.94% Cohen's kappa value, which indicates good agreement. The study supports further efforts to develop trustworthy, automated disease detection technology, which improves patient outcomes and facilitates more efficient healthcare delivery.

Introduction

Knee osteoarthritis (KOA) is a significant worldwide public health issue that impacts a large number of people and puts a significant burden on both the patients and healthcare organizations. The knee joint's articular cartilage gradually deteriorates in this disorder. It has a complicated and multidimensional etiology that includes a mix of age, obesity, joint trauma, biomechanical variables, and genetic susceptibility¹.

The loss of structural integrity results in cartilage thinning, fissuring, and eventual erosion, exposing the underlying bone. The symptoms of KOA can range widely and frequently worsen over time, f....

Protocol

This section presents a comprehensive approach designed to enhance knee osteoarthritis diagnosis and grading through the use of a modified XceptionNet model. The presented methodology is based on careful data preprocessing, thorough model architecture customization, and strong assessment techniques, all of which are intended to address the complex problems associated with knee OA imaging. In Figure 2, the flow of the model has been illustrated.

Results

Several important measures were taken throughout the model's validation phase to guarantee that it would generalize to data that had not yet been observed. Initially, the dataset is divided into training and validation sets. This is a common procedure used for evaluating the model's performance on a dataset that was not used in training. By offering separate datasets for training and validation, this separation avoided overfitting and allowed thorough assessment of model effectiveness.

Data au.......

Discussion

The study presented an automated deep learning-based technique for KOA grading using X-ray images. The model, which used an XceptionNet architecture, demonstrated notable robustness and accuracy across a range of assessment measures, suggesting that it might find use in clinical settings.

In addition to the current methodology, external dataset validation might be used to further confirm the hypothesis and evaluate the model's generalizability across various imaging conditions and demograp.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Jupyter Notebook/Colab	Project Jupyter / Google	N/A	For developing and experimenting with models interactively.
Matplotlib (Version: 3.4.3) & Seaborn (Version: 0.11.2)	Community	N/A	For data visualization and result plots.
Mendeley/Kaggle Data	Elsevier; Community	N/A	Dataset Source: Knee Osteoarthritis Severity Grading Dataset
OpenCV (Version: 4.5.5)	Intel	N/A	For preprocessing X-ray images (resizing, CLAHE, Gaussian filtering).
Python (Version: 3.8)	Python Software Foundation	N/A	Programming language used for model development.
scikit-learn (Version: 1.0.2)	Community	N/A	Used for data splitting, performance metrics, and basic ML utilities.
TensorFlow/Keras	Google/Community	N/A	Used for implementing and training the XceptionNet-based deep learning model. Tensorflow Version: 2.6.0, RRID:SCR_018932. Keras version: 2.6.0, RRID:SCR_018961
Ubuntu OS	Canonical	N/A	Operating system used for compatibility with all software tools. Version 20.04 recommended.