Detection of Knee Osteoarthritis Using Hybrid Deep Learning Model

Detection of Knee Osteoarthritis Using Hybrid Deep Learning Model

Rahul.S, Veera PrasannaKumar.S, Manoj kumar Purini, Sumathi.S

Abstract- Osteoarthritis (OA) is a not unusual situation amongst older adults that considerably affects mobility. Traditionally, diagnosing OA via visible inspection may be subjective and vulnerable to inaccuracies. However, recent improvements in pc analytical design (CAD) algorithms, specifically those making use of system mastering strategies, provide promising avenues for enhancing OA diagnosis. Nonetheless, running with complicated X-ray snap shots poses demanding situations for these algorithms.In our paper, we advocate a unique method called Discriminant Regularized Auto Encoder (DRAE) to cope with these challenges and decorate classification accuracy in OA prognosis. The DRAE technique leverages the energy of autoencoders, a kind of artificial neural network, to analyze applicable and awesome features without delay from the input facts. By incorporating the concept of "discriminatory loss" into the education system of the autoencoder, we make sure that the found out illustration captures precious facts for distinguishing among special instructions of OA.

Our research, conducted using facts from the Osteoarthritis Initiative (OAI), demonstrates the capacity of the DRAE method for early detection of knee OA. By correctly extracting discriminative capabilities from X-ray pics, our technique shows promise in improving the accuracy and efficiency of OA prognosis, ultimately leading to better patient effects and control techniques.

Keywords- Osteoarthritis,Convolution Neural Network,Knee,ResNet,SqueezeNet.

INTRODUCTION

Osteoarthritis (OA) is a common joint disorder characterized by irreversible damage to joint cartilage, with symptoms such as stiffness, inflammation, pain, and joint a its a breakdown. It is estimated that by 2050, OA will affect more than 130 million people worldwide.

Traditionally, the diagnosis of knee OA is based on the analysis of X-ray images, focusing on visual signs such as joint space narrowing, cartilage changes, and bone changes Not applicable about being commonly used, the Kellgren and Lawrence (K&L) scoring system was used in the 1990s.OA diagnosis can introduce subjectivity due to differences in expert interpretation.Attempts to predict the progression of knee OA often incorporate risk factors such as body weight, age, and sex, however, the complex interactions of these factors are still poorly understood. There is a growing body of evidence for the importance of assessing microskeletal changes, providing initial insights not detected by visual inspection. Previous methods to classify knee OA have mainly used computer vision methods, where low-level features are extracted from X-ray images These methods have had some success but are limited in their ability to capture information a it shows the discrimination in all the details. In recent years, deep tissue has emerged as a powerful tool in medical imaging, including in the diagnosis of knee OA.

Studies have used techniques such as convolutional neural networks (CNNs) and Siamese CNNs to detect and classify severe OA. However, these studies have mainly focused on the use of multiple classification tasks. In contrast, our study seeks to address the important issue of early detection of knee OA, targeting grades 0, 1, and 2 in particular. In this study, we propose a pioneering method for early detection of knee OA using X-ray images, with emphasis on the principal feature learning algorithm We propose a new method called Discriminant Regularized Auto Encoder (DRAE) a it is the standard Auto-Encoder Integrates that the discretionary is giving the loss term in the training standard. This promotion is designed to encourage scholarly representatives to include valuable information about discrimination.

LITERATURE REVIEW

There are many ways to diagnose knee disorders, including x-rays, MRIs and CT scans. These methods can be divided into three categories: classification-based, feature extraction- based, and classification-based.

One of the methods described in [15] has missed the systematic grading of knee osteoarthritis (KOA) severity. The first KOA detection system based on morphological features, mechanical electrical properties, and molecular context has been introduced [30 This system applies to MRI images and is non-ionizing, non-invasive, and in-vivo. In [31], osteoarthritis initiative (OAI) data were used to examine disease progression. Steady-state MRI with dual-echo was used for image analysis, field interpretation, and segmentation. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) were used to compare and select the best method

Various classification-based methods have been described in recent years [32], [33], [34]. Some focused on knee fractures

[32] [35] , while others described articular cartilage fractures

[35] , but did not provide quantitative comparisons.Deep learning-based systems have been explored for orthopedics in fragmentation and classification also in [36 , ] ], [38], [39], [40], and [41]. In [40], SegNet architecture was used for 2D knee images for pixel-wise semantic labeling. Later [41] achieved higher accuracy by modifying the algorithm to compute neural segmentation using conditional random forest (RF) to generate multiple classifiers.

[37] introduced slices-based segmentation and added an SSM feature to the U-Net-based segmentation framework. Even though it was true, his computer costs were high. To address this, [38] developed a simple CNN-based method called Holistically Nested Network (HNN) for ROI segmentation.

In [36], methods for supervised machine learning (SML) applications in health and biomedical fields were discussed. Deep learning techniques have shown promise in medical practice [42], [43]. In [44], a deep learning-based model was proposed to identify KOA based on minimum joint space width. Wahid and so on. [45] developed a multilevel sparse- sparse model to classify MRI scans as ACL tears but only in the coronal plane. 46]. You can refer to Table 1 in the supplied source for more details.

METHODOLOGY

Block Diagram

Fig 1 Block Diagram

2.GSM

GSM or Global System for Mobile Communications is a standard designed to facilitate mobile communications worldwide. It is one of the most widely used digital cellular network technologies. GSM networks operate in multiple distributed frequency bands around the world, ensuring that mobile devices are compatible and functional with different networks.GSM technology allows voice calls, text messages (SMS) and data transmission over mobile networks. It uses Time Division Multiple Access (TDMA) technology, where multiple users share the same frequency channel divided into time slots.One of the key features of GSM is its security infrastructure, including encryption algorithms for protecting voice and data communications.GSM has played an important role in the development of mobile communications, which formed the basis for the next generation of cellular networks including 3G and 4G/LTE but the advent of new technologies such as 5G is slowly displacing GSM networks in favor of growth size and effective communication standards.

1. Arduino UNO

HARDWARE COMPONENTS

Fig 3 GSM Module

The Arduino Uno is a popular microcontroller board based on the ATmega328P microcontroller. It is widely used in various DIY electronics projects and prototypes due to its conveniene, ease of use and affordability. The Uno has digital and analog input/output pins that can be easily programmed using the Arduino IDE (Integrated Development Environment), supporting simplified C and C++ programming languages It also includes a USB connection for programming and power, and power jacks for external power sources. The Arduino Uno is a versatile tool for beginners and advanced users, enabling a wide range of applications, from simple LEDs to complex robotics and IoT applications.

Fig 2 Arduino UNO

SOFTWARE COMPONENTS

1. MATLAB

   MATLAB, short for "Matrix Laboratory," is a high-level programming language and interactive environment developed by MathWorks. It is widely used in industry and education for numerical calculations, data analysis and visualization. MATLAB provides a comprehensive set of tools and functions for numerical applications, algorithm development, modeling, simulation, and prototyping.

2. Arduino IDE

The Arduino IDE (Integrated Development Environment) is a software program for programming the Arduino microcontroller board. It provides a user-friendly way to write, compile and export to Arduino boards, making it accessible to both beginners and experienced users Some of the key features include a simple code editor with syntax highlighting and auto-closing, a library for managing code libraries Manager, a serial monitor for debugging, support for multiple Arduino-compatible boards, built-in examples and tutorials, and cross- platform compatibility with Windows , macOS, and Linux are compatible. Overall, the Arduino IDE simplifies the Arduino board programming process, making

it a favorite for hobbyists, educators, and professionals working in the electronics industry.

OPERATION

Osteoarthritis (OA) of the knee is a common degenerative joint disease characterized by degeneration of the underlying cartilage and bone, causing pain, stiffness, and dysfunction in affected individuals, early detection of knee OA is essential for timely intervention and effective management to maintain disease reduction and improve patient outcomes and. Medical imaging modalities such as X-ray and magnetic resonance imaging (MRI) provide detailed anatomical information and structural changes in the knee joint.

In recent years, advances in artificial intelligence (AI) and machine learning techniques have revolutionized medical image analysis, providing practical and accurate solutions for disease diagnosis and findings -Demonstrates remarkable performance in medical imaging. This article explores the working principles and application principles of CNN in knee osteoarthritis diagnosis, and outlines the main steps involved in the process, from data acquisition to pre-processing to model training and evaluation Furthermore, is a CNN for knee OA detection We discuss challenges and future guidelines for the development and implementation of – based methods, emphasizing the potential impact on clinical practice and patient care.

Overview of Convolutional Neural Networks (CNNs) Convolutional neural networks (CNNs) are models of deep learning inspired by the structure and function of the visual cortex in biology. CNNs are specifically designed for processing and analyzing visual information, making them ideally suited for tasks such as image recognition, object recognition, classification, etc. A typical CNN is structured at several levels , including convolutional layers, pooling layers, and fully connected layers Performs specific tasks to perform

Data acquisition and preprocessing

The first step in developing a CNN-based system for the diagnosis of knee osteoarthritis is to obtain medical image data including X-ray or MRI scans of the knee joint These images are data that it is inserted for the CNN model and is important for training and its evaluation efficiency. Clinical image datasets for the diagnosis of knee OA can be obtained from a variety of sources, including hospitals, research institutes and publicly accessible repositories.

Before inputting the imaging data into the CNN model, a preprocessing step is usually applied to improve the quality and accuracy of the images. Common preprocessing techniques include sizing, normalization, and noise reduction. Resizing the images resizes the images to standard size, making them consistent throughout the data set. The goal of normalization is to standardize the intensity values of pixels in images, which facilitates more robust and efficient training of the CNN model. Various noise reduction techniques, such as Gaussian blurring or medium filtering, can be used to remove artifacts and improve image clarity

Best construction and training

Once the imaging data are obtained and preprocessed, the next step is to develop a CNN model for the diagnosis of knee osteoarthritis and to train the structure of the CNN model plays an important role in its performance and effectiveness in treatment in the analysis of photographs. Although there are various CNN architectures such as AlexNet, VGG and ResNet, researchers usually tailor the architecture according to the specific requirements of the task at hand.

Model analysis and verification

After a CNN model is trained, it is important to evaluate its performance and overall on unseen data to verify its effectiveness in real-world applications It provides unbiased performance evaluation and the ability to detect knee osteoarthritis equally in other patients

In model evaluation, performance metrics such as accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score etc. are calculated to quantitatively evaluate the performance of the model Furthermore, receiver operating characteristic (ROC) curve and precision-recall (PR) curve. The curve is often used to evaluate the trade-off between true-positive rates and false- positive rates in decision requirements.

ADVANTAGES

The use of Convolutional Neural Networks (CNNs) in hybrid deep learning offers several advantages:

1. Feature extraction: CNNs excel in automatically identifying hierarchical features from raw input data, especially images. By using convolutional layers, CNNs can efficiently extract meaningful features from complex input data, which is useful for tasks such as image classification, object recognition, and image segmentation.

2. Spatial Hierarchies: CNNs assume spatial hierarchies of objects in data. Convolutional layers learn local patterns or features at lower layers and in turn combine them to form higher-level representations at deeper layers. This hierarchical feature extraction is well suited for tasks where spatial relationships are important, such as image analysis and computer vision.

3. Parameter sharing: CNNs use parameter sharing, where visible filters (kernels) are applied to the entire input image or feature map. This sharing reduces the number of parameters in the network, making CNNs computationally efficient and scalable, especially for large data sets and complex models.

4. Translation-invariant: CNNs are inherently translation- invariant, ie. it can recognize patterns or objects regardless of their position in the input data This property makes CNNs robust to changes in object location and orientation, which are object recognition internal images, . It is also useful for tasks such as research.

5. Transfer learning: CNNs trained on big data (e.g., ImageNet) can be optimized or used as feature extractors for downstream tasks with limited label data This transfer learning approach can use a CNN.

RESULT

Convolutional neural networks (CNNs) offer a promising solution for the detection of knee osteoarthritis (OA) through their ability to efficiently process medical image data. However, challenges remain, including limited data, interpretability of the CNN model, and the need for standardized assessment scales Future directions include to use multiple imaging data, integrate clinical data, and address legal and ethical considerations for real-world application.CNNs Choking It shows that OA can be diagnosed, but challenges such as data available and model definitions are still available. Future work will focus on integrating imaging modalities, incorporating clinical data, and addressing regulatory issues for practical application

Figure 4(a) Input Image

Figure 4(b) Grey Scale Image

Figure 4(c) Sparse Model

Figure 4(d) Segmented Image

Figure 4(e) Iteration Cylce

Figure 4(f) Classiification Result CONCLUSION

In this study, we proposed a novel method for osteoporosis detection in thermal images based on a deep hybrid learning algorithm. Our method includes preprocessing of thermal images, segmentation of the region of interest, and feeding into a hybrid model, which is trained to classify the thermal image as normal or abnormal based on the presence of osteoarthritis and for obtained Can ResNet blocks allow training deep networks, while SqueezeNet blocks reduce dimensionality of feature maps and allow smaller model size Global average pooling level and production level are used to calculate the final result of the model. 93, 0.93, and AUC respectively. outperforming several state-of-the-art approaches..The proposed method could be used as a reliable non-invasive early detection and diagnosis tool for osteoporosis, thus aiding clinical staff to provide appropriate and effective treatment to patients.

Figure 5 Conclusion Chart

REFERENCE

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