Improved Covid-19 Detection Using PCA, GLCM, and CNN: Enhancing Feature Extraction and Classification Models

Problem Definition

After conducting a thorough literature review, it is evident that the current COVID-19 detection models are facing significant challenges that hinder their effectiveness. One of the major issues is the degradation of detection rate and performance, which can be attributed to the limitations of machine learning classifiers commonly used in these models. These ML classifiers struggle to handle large datasets, often resulting in overfitting and reduced accuracy in identifying COVID-19 cases. Additionally, the lack of focus on texture features in existing models is a critical gap, as these features are crucial for accurate detection of the virus. Even in cases where feature extraction techniques are applied, there are concerns about low computational speeds when analyzing large-scale images for COVID-19 detection.

Overall, the existing COVID-19 detection methods face limitations in terms of performance, dataset handling, and utilization of texture features, highlighting the need for a new and improved approach to overcome these challenges. The development of a more robust and efficient detection model is essential in addressing the current shortcomings and improving the accuracy and reliability of COVID-19 diagnosis.

Objective

The objective of the study is to develop a new and improved COVID-19 detection model that addresses the limitations of existing approaches. This new model aims to enhance the detection rate while reducing computational complexity by utilizing Principal Component Analysis (PCA) and Gray-Level Co-occurrence Matrix (GLCM) for feature extraction from chest X-ray images. By combining these techniques with a Convolutional Neural Network (CNN) as a deep learning classifier, the study seeks to improve the accuracy and reliability of COVID-19 diagnosis.

Proposed Work

After analyzing the current literature on COVID-19 detection models, it was evident that existing approaches were facing challenges in terms of accuracy and computational complexity. Most models relied on machine learning classifiers that struggled with large datasets and often led to overfitting. Additionally, the lack of focus on extracting texture features from medical images posed a significant obstacle to accurate detection of COVID-19. To address these issues, a new and improved COVID-19 detection model was proposed in this study. The main objective of the project is to enhance the detection rate while reducing computational complexity.

To achieve this goal, the proposed approach involved implementing Principal Component Analysis (PCA) and Gray-Level Co-occurrence Matrix (GLCM) for feature extraction from chest X-ray images. PCA was utilized to categorize data into lower dimensions based on eigenvectors with high eigenvalues. GLCM was applied to extract second-order statistical textural features from the x-ray images. By combining these techniques, the model aimed to extract crucial textual features efficiently. Furthermore, the use of Convolutional Neural Network (CNN) as a deep learning classifier was deemed essential for handling large and non-linear datasets.

CNN was chosen over Recurrent Neural Network (RNN) due to its superior performance in image analysis. By training the CNN classifier with features extracted using PCA-GLCM, a robust and accurate COVID-19 detection model was developed.

Application Area for Industry

This project can be effectively implemented across various industrial sectors such as healthcare, pharmaceuticals, and biotechnology. The proposed solutions address specific challenges faced by these industries in detecting and recognizing COVID-19 in the early stages. By utilizing advanced techniques like PCA for feature extraction and CNN for classification, the project aims to improve the accuracy rate and decrease computational complexity. Implementing these solutions in industries can lead to more efficient and accurate detection of COVID-19 in patients, ultimately improving patient care and treatment outcomes. Additionally, the ability to handle large and complex datasets using DL classifiers can streamline the diagnostic process and help in making timely and informed decisions.

Overall, the benefits of implementing these solutions include enhanced detection rates, reduced computational burden, and improved overall performance in the fight against COVID-19.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of medical image analysis and disease detection, specifically in the context of COVID-19. By introducing a new and improved COVID-19 detection model that addresses the limitations of existing systems, researchers, MTech students, and PHD scholars can benefit from this work in various ways. The relevance of this project lies in its innovative approach towards feature extraction and classification in medical chest X-ray images. By utilizing techniques such as PCA and GLCM for feature extraction and CNN for classification, this project offers a more efficient and accurate method for detecting COVID-19 in patients. The integration of these algorithms not only enhances the detection rate but also reduces computational complexity, offering a practical solution for handling large datasets.

Researchers in the field of medical image analysis can leverage the code and literature of this project to explore new methods for disease detection and diagnosis. MTech students can use this work as a reference for developing their own research projects related to medical imaging and deep learning applications. PHD scholars can further extend this project by exploring additional algorithms and technologies to improve the accuracy and performance of COVID-19 detection models. The future scope of this project includes integrating other advanced deep learning techniques, exploring different feature extraction methods, and enhancing the overall performance of the COVID-19 detection model. By continuously refining and expanding upon the proposed approach, researchers can contribute towards the development of more robust and efficient systems for disease detection in medical imaging.

Algorithms Used

PCA is used to reduce the dimensionality of the data by identifying the most important features through eigenvectors and eigenvalues of the covariance matrix. GLCM is used to extract second order statistical textural features from the X-ray images, which are essential for accurate detection. CNN is employed as a deep learning classifier due to its ability to handle large and complex datasets, producing more accurate results on images compared to RNN. By combining PCA, GLCM, and CNN, the proposed model aims to improve accuracy and efficiency in COVID-19 detection by extracting relevant features and classifying the X-ray images effectively.

Keywords

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SEO Tags

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