Automated Detection of COVID-19 in X-Ray Images using Transfer Learning and Deep Learning Techniques

Problem Definition

The current problem in the domain of COVID-19 prediction using x-ray images revolves around the sensitivity issues caused by noise, specifically Gaussian noise and poison noise. These disturbances hinder the accurate extraction of data, which in turn affects the overall categorization accuracy of the system. Additionally, the use of Histogram of Oriented Gradients (HOG) for feature retrieval has shown promise but is limited by its susceptibility to picture rotations. This limitation poses a significant challenge for classification stages, impacting the reliability of the system. Moreover, the reliance on traditional machine learning (ML) algorithms such as SVM and KNN for classification has proven effective but inefficient when handling large datasets.

The extended processing and execution times of these algorithms become a bottleneck in the system's performance, highlighting the need for more efficient methods in COVID-19 prediction using x-ray images.

Objective

The objective is to improve the accuracy and efficiency of COVID-19 prediction using x-ray images by addressing the sensitivity issues caused by noise, enhancing feature extraction with GLCM and LBP techniques, and implementing a deep learning model for classification. This novel approach aims to overcome the limitations of existing detection methods, such as susceptibility to picture rotations and inefficiencies in handling large datasets, ultimately leading to higher levels of accuracy in identifying the virus.

Proposed Work

The proposed work aims to address the limitations of existing COVID-19 detection methods that utilize x-ray images by implementing a novel deep learning model. By denoising the medical images using DnCNN, improving feature extraction with GLCM and LBP techniques, and employing a deep learning architecture for classification, the model seeks to enhance accuracy and efficiency. The model will undergo stages such as data collection, pre-processing, data separation, and classification to effectively identify COVID-19 in patients. By leveraging deep learning techniques, the proposed model aims to overcome the challenges posed by noise sensitivity and rotation issues in traditional detection systems. The use of GLCM and LBP techniques will help mitigate the limitations of HOG feature extraction and improve the system's ability to handle rotating images.

GLCM, which focuses on the textural relationship between pixels based on second-order statistics, will play a crucial role in feature extraction. Additionally, the deep learning approach will enable the model to efficiently process and classify large medical datasets, leading to improved classification accuracy. By integrating these advancements into the conventional COVID-19 detection paradigm, the proposed model is expected to achieve higher levels of accuracy in identifying the virus.

Application Area for Industry

This project can be utilized in various industrial sectors such as healthcare, pharmaceuticals, and medical imaging. The challenges faced by these industries include the inaccuracies in categorizing COVID-19 in patients due to noise sensitivity in x-ray images, limitations of traditional feature extraction techniques like HOG, and the inefficiency of classical machine learning algorithms in handling large datasets. By implementing deep learning techniques, denoising methods, and alternative feature extraction approaches like GLCM and LBP, this project offers solutions to these challenges. The benefits of applying the proposed solutions in different industrial domains include improved accuracy in COVID-19 detection, enhanced classification performance, and efficient processing of large medical datasets. By utilizing deep learning for data analysis and incorporating advanced feature extraction techniques, industries can overcome the limitations of existing detection systems and achieve a higher level of accuracy in categorizing medical conditions.

Ultimately, the implementation of these solutions can lead to more effective treatment strategies, better patient outcomes, and advancements in medical research.

Application Area for Academics

This proposed project has the potential to enrich academic research, education, and training in the field of medical imaging and COVID-19 detection. By addressing the limitations of existing methods through the use of deep learning techniques and alternative feature extraction methods such as GLCM and LBP, the project can contribute towards innovative research methods in medical image analysis. The relevance of this project lies in its application to improve the accuracy of COVID-19 detection using x-ray images, which is crucial in the current healthcare landscape. This project can serve as a valuable resource for researchers, MTech students, and PHD scholars in the field of machine learning, medical imaging, and healthcare technology. Researchers can utilize the code and literature from this project to further explore deep learning techniques, denoising methods, and feature extraction for medical image analysis.

MTech students can learn from the implementation of algorithms such as DnCNN, CNN, GLCM, and LBP to enhance their understanding of image processing and classification. The future scope of this project includes expanding the dataset, exploring other deep learning models, and collaborating with healthcare professionals to validate the results. Overall, this project has the potential to advance research in the field of medical imaging and contribute to the development of more accurate and efficient COVID-19 detection methods.

Algorithms Used

DnCNN is used for denoising the medical images in the project to remove noise and abnormalities present in X-ray images, improving the accuracy of COVID-19 detection. LBP and GLCM are utilized for feature extraction to address the sensitivity of the model to rotating pictures. GLCM helps in analyzing the textural relationship between pixels using second-order statistics, while LBP aids in overcoming issues related to image rotation. CNN is employed for managing the substantial medical dataset and improving the classification accuracy of the system. By integrating these algorithms, the proposed model aims to enhance the efficiency and accuracy of COVID-19 detection through deep learning techniques.

Keywords

SEO-optimized keywords: 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, Gaussian noise, Poison noise, Data extraction, Histogram of Oriented Gradients (HOG), ML algorithms, SVM, KNN, Rotation sensitivity, Gray level Co-occurrence matrix (GLCM), Local Binary Pattern (LBP), Classification accuracy, Deep Learning methodology, Noise reduction, Textural relationship, Second-order statistics, Healthcare technology, Biomedical image analysis.

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