Classification of COVID-19 in chest X-ray images using deep neural network with enhanced feature selection and extraction

Problem Definition

The current research focuses on improving the classification of COVID-19 from chest X-ray images using the DE-TRAQ deep convolution neural network. The main issue at hand is the model's inefficiency, which stems from the flawed feature extraction process and the lack of ability to select the most appropriate features from the dataset, resulting in increased complexity. Furthermore, the existing model lacks accuracy, sensitivity, specificity, precision, and F1 score for COVID-19 classification. These limitations highlight the pressing need for enhancements in the classification process to better identify and differentiate COVID-19 cases from chest X-ray images, ultimately improving diagnostic accuracy and patient outcomes.

Objective

The objective of the research is to enhance the classification of COVID-19 from chest X-ray images using the DE-TRAQ deep convolution neural network by improving feature extraction, optimizing feature selection, and upgrading the classification model. This will be achieved through the implementation of an upgraded Salp-SWAM algorithm for feature selection, transitioning from ImageNet to AlexNet for feature extraction, and architectural modifications to the DE-TRAQ model. By addressing the inefficiencies of the current model, the research aims to improve accuracy, sensitivity, specificity, precision, and F1 score for COVID-19 classification, ultimately leading to better diagnostic accuracy and patient outcomes.

Proposed Work

To address the inefficiencies in classifying COVID-19 from chest X-ray images using the DE-TRAQ deep convolution neural network, the proposed work focuses on enhancing feature extraction, optimizing feature selection, and upgrading the classification model. By introducing an upgraded Salp-SWAM algorithm for feature selection and transitioning from ImageNet to AlexNet for feature extraction, the model aims to improve accuracy, sensitivity, specificity, precision, and F1 score for COVID-19 classification. The architectural modifications to the DE-TRAQ model, including increased depth and adjustments to filters and max pooling layers, are intended to create a more effective classification model for COVID-19 diagnosis via chest X-ray images. By adopting these improvements, the research seeks to address the current model's limitations and achieve better performance outcomes for COVID-19 classification. The rationale behind choosing the Salp-SWAM algorithm for feature selection lies in its ability to select optimal features for training the network, thereby reducing complexity and improving classification accuracy.

The decision to enhance feature extraction by incorporating additional textual and spatial features alongside the original extracted features aims to provide a more comprehensive set of features for the model to learn from. The transition from ImageNet to AlexNet for feature extraction ensures a more efficient and effective process, leading to better overall performance. The architectural modifications to the DE-TRAQ model were based on the need to increase the model's depth and make adjustments to layers to better capture the features relevant to COVID-19 classification. By carefully selecting these techniques and algorithms, the proposed work aligns with the objectives of improving the current model's deficiencies and enhancing its performance for COVID-19 classification from chest X-ray images.

Application Area for Industry

This project can be applied in various industrial sectors such as healthcare, pharmaceuticals, and diagnostics. The proposed solutions can be utilized to enhance the accuracy and efficiency of classifying diseases or abnormalities from medical imaging data, not limited to COVID-19 but including other conditions as well. By improving the feature selection process and refining the classification model, the project addresses the challenges faced in accurately diagnosing diseases from medical images, leading to better patient care, faster diagnoses, and potentially reducing human error in a medical setting. These solutions can benefit industries by providing more reliable and faster diagnostic tools, ultimately improving patient outcomes and overall healthcare services.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of medical image analysis and machine learning. By tackling the inefficiencies in classifying COVID-19 from chest X-ray images, this research introduces a novel approach that can potentially revolutionize the accuracy and effectiveness of such diagnoses. Researchers, MTech students, and PHD scholars working in the domain of medical image analysis and deep learning can benefit greatly from the code and literature generated by this project. By using the upgraded Salp-SWAM algorithm for feature selection and implementing architectural modifications to the DE-TRAQ model, researchers can explore innovative research methods, simulations, and data analysis techniques within educational settings. Moreover, the utilization of MATLAB and advanced algorithms like Salp-SWAM, AlexNet, and DE-TRAQ can provide a robust foundation for developing new approaches in medical image classification and disease diagnosis.

In terms of future scope, this project opens up avenues for further refinement and optimization of deep learning models for medical imaging tasks. Researchers can explore the potential applications of these techniques in other medical conditions for accurate diagnosis and treatment planning. Additionally, the insights gained from this project can lay the groundwork for collaborative research endeavors and interdisciplinary studies that bridge the gap between computer science, healthcare, and medical diagnostics.

Algorithms Used

The primary algorithm used for optimizing feature selection is the Salp-SWAM algorithm. This algorithm facilitates the selection of the most relevant features for training the neural network, enhancing the accuracy and efficiency of the classification model. Additionally, improvements were made to the feature extraction model by transitioning from ImageNet to AlexNet within the convolutional neural network (CNN). This upgrade allowed for better extraction of features from the chest X-ray images, incorporating textual and spatial features alongside the original extracted features. Furthermore, modifications were applied to the DE-TRAQ model for classification, including increased depth, changes to filters, and adjustments to max pooling layers.

These enhancements resulted in a more effective and precise classification model for detecting COVID-19 using chest X-ray images.

Keywords

SEO-optimized keywords: COVID-19 classification, chest X-ray images, DE-TRAQ deep convolution neural network, feature extraction, Salp-SWAM algorithm, AlexNet, ImageNet, deep learning, biomedical applications, AI in healthcare, MATLAB, accuracy, sensitivity, precision, F1 score, specificity, COVID-19 diagnosis, convolutional neural network, healthcare technology, medical imaging, deep learning algorithms, image processing, algorithm optimization.

SEO Tags

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