Integrated Deep Learning Model for Medical Image Analysis Using DnCNN Denoising, GLCM, LBP, and CNN

Problem Definition

The existing literature has revealed several key limitations and problems in the domain of COVID-19 prediction using x-ray images. One major issue is the sensitivity of x-ray images to Gaussian and poison noise, which impacts the accuracy of data extraction and subsequently affects the system's categorization accuracy. Additionally, the use of Histogram of Oriented Gradients (HOG) for feature extraction has proven effective but is hindered by its susceptibility to picture rotations, making it less reliable for classification stages when images rotate. Furthermore, traditional machine learning (ML) algorithms such as SVM and KNN have shown promising results in classification tasks, but their efficiency suffers when dealing with large datasets, resulting in lengthy processing and execution times. Therefore, there is a clear need to explore the implementation of deep learning (DL)-based algorithms that can handle huge datasets efficiently in order to improve classification accuracy within this domain.

Objective

The objective of this study is to develop a novel deep learning model to address the limitations in existing studies related to COVID-19 prediction using x-ray images. The proposed model will focus on denoising medical images using the DnCNN technique to improve feature extraction accuracy. Additionally, the model will incorporate GLCM and LBP techniques for enhanced feature extraction. Utilizing deep learning algorithms for classification, the aim is to overcome efficiency issues faced by traditional ML algorithms when handling large datasets. By combining denoising, feature extraction, and classification techniques, the objective is to accurately predict COVID-19 based on medical images.

Proposed Work

In this work, we aim to address the limitations identified in existing studies by proposing a novel model that leverages deep learning techniques. The proposed model will focus on denoising sample medical images using the DnCNN deep learning technique. By eliminating noise from the images, we aim to improve the accuracy of feature extraction. To achieve this, we will enhance the feature extraction model by incorporating Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) techniques. GLCM is known for its ability to analyze the textural relationship between pixels based on second-order statistics, while LBP is an algorithm that extracts texture features by encoding pixel neighbourhood structures.

Additionally, we plan to utilize a deep learning architecture for the classification stage of the proposed model. Traditional machine learning algorithms such as SVM and KNN have shown promising results in classification tasks, but they face efficiency issues when dealing with large datasets. By implementing deep learning algorithms, we aim to overcome these challenges and improve classification accuracy. The deep learning approach will allow us to efficiently handle the substantial medical dataset and enhance the overall performance of the proposed model. By combining denoising, feature extraction, and classification techniques using deep learning methods, we aim to develop a comprehensive solution that can accurately predict COVID-19 in individuals based on medical images.

Application Area for Industry

This project can be utilized in the healthcare industry to improve the accuracy of COVID-19 diagnosis using advanced image processing techniques. By addressing the noise sensitivity issues in x-ray images and implementing feature extraction methods like GLCM and LBP, the accuracy of categorizing COVID-19 cases can be significantly enhanced. Furthermore, by incorporating deep learning algorithms to handle large medical datasets efficiently, the processing and execution times can be reduced, leading to faster and more accurate diagnosis outcomes. Implementing these solutions can result in quicker and more precise identification of COVID-19 cases, ultimately improving patient care and reducing the burden on healthcare systems. Additionally, this project's proposed solutions can also be applied in industries that rely on image processing and classification, such as manufacturing and surveillance.

By leveraging the GLCM and LBP feature extraction methods, these industries can improve the accuracy of image analysis and enhance pattern recognition capabilities. Furthermore, the use of deep learning algorithms can help in efficiently handling large datasets and reducing processing times, leading to more accurate and timely decision-making. Implementing these solutions in manufacturing and surveillance industries can result in improved quality control, enhanced security measures, and overall operational efficiency.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in various ways. By addressing the limitations of traditional methods used in medical image analysis for COVID-19 detection, the project can contribute to innovative research methods and data analysis techniques. Researchers in the field of medical imaging and computer vision can benefit from the implementation of deep learning algorithms such as DnCNN, CNN, GLCM, and LBP in the proposed model. The utilization of GLCM and LBP for feature extraction can enhance the accuracy of categorization systems by overcoming noise sensitivity issues and rotation problems associated with other methods like HOG. Additionally, the incorporation of deep learning techniques will allow for more efficient handling of large datasets, improving classification accuracy and reducing processing times.

This can open up new avenues for research in the detection and diagnosis of COVID-19 using advanced image processing algorithms. MTech students and PhD scholars can utilize the code and literature of this project to learn about the practical implementation of deep learning algorithms in medical image analysis. By exploring the methodologies and results of the proposed model, students can gain valuable insights into the application of AI in healthcare and potentially develop their own research projects based on similar techniques. The relevance of this project lies in its potential to revolutionize the field of medical imaging for COVID-19 detection through the integration of deep learning and advanced feature extraction methods. Researchers can explore further advancements in this area, while students can leverage this work for educational purposes and training in cutting-edge research techniques.

The future scope of this project includes exploring additional deep learning architectures and optimization methods to further improve the accuracy and efficiency of COVID-19 detection systems.

Algorithms Used

In the proposed work, a novel model will be suggested integrating deep learning techniques to overcome constraints in medical image analysis. The Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) approaches will be utilized to address textural features in images. GLCM calculates second-order statistics to determine pixel relationships, while LBP extracts texture features by encoding pixel neighbourhood structures. Additionally, a deep learning approach, specifically the DnCNN and CNN algorithms, will be employed to effectively process the extensive medical dataset, contributing to improved accuracy and efficiency in achieving the project's objectives.

Keywords

SEO-optimized keywords: COVID-19 detection, Transfer learning, X-ray images, Gaussian noise, Poison noise, Data extraction, Categorization accuracy, Histogram of Oriented Gradients (HOG), Picture rotations, Feature extraction, Classification stages, ML algorithms, SVM, KNN, Deep learning algorithms, Gray level Co-occurrence matrix (GLCM), Local Binary Pattern (LBP), Second-order statistics, Texture analysis algorithm, Image processing, Computer vision, Medical dataset, Convolutional Neural Networks (CNNs), Healthcare technology, Biomedical image analysis, Artificial intelligence.

SEO Tags

COVID-19 detection, Transfer learning, X-ray images, Convolutional Neural Networks (CNNs), Deep learning, Medical imaging, Computer-aided diagnosis, Feature extraction, Image classification, Pre-trained models, Fine-tuning, Data augmentation, Medical diagnosis, Disease identification, Healthcare technology, Biomedical image analysis, Artificial intelligence, Study gaps, Gaussian noise, Poison noise, Data extraction, Categorization accuracy, Histogram of Oriented Gradients (HOG), Picture rotations, ML algorithms, SVM, KNN, DL-based algorithms, Gray level Co-occurrence matrix (GLCM), Local Binary Pattern (LBP), Texture analysis, Image processing, Computer vision, Deep learning based approach, Second-order statistics, Texture features, Pixel neighbourhoods, Medical dataset, Research scholar, PHD student, MTech student, Research topic, Search terms, Search phrases.