Automated Knee Osteoarthritis Prediction and Classification from X-ray Images Using Deep Learning

IEEE BASE PAPER TITLE:

KOA-CCTNet: An Enhanced Knee Osteoarthritis Grade Assessment Framework Using Modified Compact Convolutional Transformer Model

IEEE BASE PAPER ABSTRACT:

Knee osteoarthritis (KOA) is a prevalent condition characterized by gradual progression, resulting in observable bone alterations in X-ray images. X-rays are the preferred diagnostic tool for their ease of use and cost-effectiveness. Physicians use the Kellgren and Lawrence (KL) grading system to understand the severity of an individual condition of KOA. This system categorizes the disease from normal to a severe stage. Early detection of the condition with this approach enables knee deterioration to be slowed down with therapy. In this study, we aggregated four datasets to generate an extensive dataset comprising 110,232 raw images by applying an augmentation technique called deep convolutional generative adversarial network (DCGAN).We employed advanced image pre-processing methods (adaptive histogram equalization (AHE), fast non-local means), including image resizing, to generate a substantial dataset and enhance image quality. Our proposed approach involved developing a modified compact convolutional transformer (CCT) model known as KOA-CCTNet as the foundational model. We further investigated optimal configurations by adjusting various parameters and hyperparameters in the final model to handle large datasets and address training time concerns efficiently. We investigated optimizing its configurations by adjusting numerous parameters and hyperparameters to efficiently manage extensive data and address concerns related to training time. Simulation results indicated that our proposed model outperforms other transfer learning models (Swin Transformer, Vision Transformer, Involutional Neural Network) in terms of accuracy. The test accuracy for the ResNet50, MobileNetv2, DenseNet201, InceptionV3, and VGG16 was 80.77%, 79.98%, 80.23%, 76.89%, and 79.58%, respectively. All of them were surpassed by our proposed KOA-CCTNet model, which had a test accuracy of 94.58% while classifying KOA X-ray images. Furthermore, we reduced the number of images to assess the model’s performance and compared it to existing models. However, by employing a large datahub, our proposed approach provides a unique and effective way to diagnose KOA grades with satisfying results.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

VGG16 Model, MobileNetV2 Model.

OUR PROPOSED PROJECT ABSTRACT:

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts the quality of life. Early and accurate diagnosis is critical for effective management and treatment. This project, titled “Automated Knee Osteoarthritis Prediction and Classification from X-ray Images Using Deep Learning,” presents a novel approach for diagnosing and grading knee OA using deep learning models.

The system is developed using Python for backend computation, Flask as the web framework, and HTML, CSS, and JavaScript for the frontend. Two state-of-the-art deep learning models, VGG16 and MobileNetV2, have been employed to classify X-ray images of knee joints into five categories based on the Kellgren and Lawrence grading system: Normal, Doubtful, Mild, Moderate, and Severe.

The dataset consists of 1,650 high-quality 8-bit grayscale X-ray images collected from reputable hospitals and diagnostic centers using the PROTEC PRS 500E X-ray machine. Each image has been manually annotated by two medical experts for accuracy and reliability.

The VGG16 model achieved an impressive train accuracy of 93% and a test accuracy of 92%, while the MobileNetV2 model outperformed with a train accuracy of 96% and a test accuracy of 96%. Performance metrics, including Accuracy, Precision, Recall, F-Measure, and a Confusion Matrix, have been computed to evaluate each model comprehensively.

The findings of this study highlight the potential of deep learning in automating the detection and grading of knee osteoarthritis, offering a cost-effective and scalable solution for clinical use. The system’s superior performance, particularly with the MobileNetV2 model, demonstrates its applicability in real-world diagnostic settings.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 8 GB.

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.12.0.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Mushrat Jahan, Md. Zahid Hasan, Ismot Jahan Samia, Kaniz Fatema, Md. Awlad Hossen Rony, Mohammad Shamsul Arefin, and Ahmed Moustafa, “KOA-CCTNet: An Enhanced Knee Osteoarthritis Grade Assessment Framework Using Modified Compact Convolutional Transformer Model”, in IEEE Access, Volume 12, pp. 107719-107741, 2024.

Python IEEE Projects

Automated Knee Osteoarthritis Prediction and Classification from X-ray Images Using Deep Learning