Hybrid Feature Extraction and ISSA based Feature Selection for COVID-19 Detection with Deep Learning Architecture.

Problem Definition

Upon reviewing the literature surrounding deep learning-based mechanisms for COVID-19 detection, it becomes apparent that current systems are facing several critical challenges. One key limitation is the complexity of existing detection systems, leading to potential issues in interpretation and application. Moreover, the accuracy rates of these systems are not always optimal, posing risks of misdiagnosis and improper treatment. The high dimensionality of image data further exacerbates these challenges, making it difficult to process and analyze effectively. Additionally, the presence of variability and overlapping features in chest X-ray images introduces another layer of complexity, hindering the accurate differentiation between COVID-19 and other respiratory conditions.

As a result, there is a critical need for more sophisticated models that can adeptly capture subtle patterns within the data and provide accurate, reliable detection of COVID-19.

Objective

The objective of this study is to address the limitations of current deep learning-based mechanisms for COVID-19 detection by developing a more sophisticated model. This model aims to accurately differentiate between COVID-19 and other respiratory conditions by adeptly capturing subtle patterns within chest X-ray image data. The proposed work includes improvements in feature extraction and selection phases, employing an advanced deep learning architecture for image classification. By combining features extracted from a pre-trained DL architecture and statistical techniques, along with utilizing nature-inspired optimization algorithms, the goal is to enhance the accuracy and reliability of COVID-19 detection. This study aims to provide a more effective and efficient system for diagnosing COVID-19, ultimately contributing to improved patient care and outcomes.

Proposed Work

To introduce novelty into our work, we have made improvements in both the FE and FS phases of the proposed model, along with employing an advanced DL architecture for image classification. In the FE phase, features are extracted using two distinct approaches. Firstly, a feature set is obtained by leveraging the pre-trained DL architecture ALexNet. Secondly, we incorporate statistical, GLCM (Gray-Level Co-occurrence Matrix), and PCA (Principal Component Analysis) techniques to derive a second feature set. To optimize feature selection, nature-inspired ISSA (Improved Salp Swarm Algorithm) optimization algorithm is utilized.

Additionally, PCA is applied to the first feature set to select only relevant features and reduce dataset dimensionality. These two feature sets are then merged to form a final set of features, which serves as the basis for training the model. Moving on to the classification phase, an improved layered DL network architecture is employed to identify and classify chest X-ray images into three classes: normal, COVID, and pneumonia. The layers within the proposed DL framework are thoughtfully designed to achieve desired results.

Application Area for Industry

This project can be effectively used in various industrial sectors, including healthcare, pharmaceuticals, and biotechnology. The proposed solutions address challenges faced by industries dealing with medical imaging analysis, specifically in the context of COVID-19 detection. By leveraging advanced deep learning techniques, the project aims to improve accuracy rates and reduce complexities associated with existing detection systems. The model's ability to effectively capture subtle patterns in chest X-ray images and differentiate COVID-19 from other respiratory conditions provides immense value to industries striving for accurate and efficient disease diagnostics. Implementing the proposed solutions in different industrial domains can lead to several benefits.

For instance, in healthcare, the enhanced model can streamline the diagnostic process by providing more accurate and reliable results, ultimately improving patient outcomes. In pharmaceuticals and biotechnology, the model can aid in drug development research by facilitating the identification of potential COVID-19 cases for clinical trials. Overall, the project's solutions offer a versatile and impactful approach to addressing critical challenges in various industrial sectors, ultimately enhancing operational efficiency and decision-making processes.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training by introducing a novel and enhanced deep learning model for the detection of COVID-19 from chest X-ray images. This project addresses the existing limitations of complexity, lower accuracy rates, and difficulties in managing high-dimensional image data by incorporating advanced DL architecture and feature extraction techniques. Researchers, MTech students, and Ph.D. scholars in the field of medical imaging and artificial intelligence can utilize the code and literature of this project for their work.

The project covers technologies such as ISSA optimization algorithm, PCA, AlexNet, and CNN, offering a comprehensive understanding of sophisticated models for image classification in the healthcare domain. This project's relevance lies in its potential applications for pursuing innovative research methods, simulations, and data analysis within educational settings. By leveraging nature-inspired optimization algorithms and advanced DL architectures, researchers can explore new avenues in medical image analysis, leading to more accurate and efficient COVID-19 detection systems. Furthermore, the project's future scope includes expanding the research domain to include other respiratory conditions and integrating additional datasets to enhance the model's performance. Overall, this project offers a valuable contribution to academic research, education, and training in the realm of medical imaging and deep learning algorithms.

Algorithms Used

The ISSA algorithm is utilized to optimize feature selection in the proposed model, enhancing the efficiency of the classification process by selecting only relevant features. PCA is employed in conjunction with the AlexNet pre-trained deep learning architecture to extract features from the input data, improving the accuracy of the model by capturing important patterns in the images. The CNN algorithm from DeTrac is used in the classification phase to classify chest X-ray images into three classes - normal, COVID, and pneumonia. These algorithms collectively contribute to the project's objective of accurately classifying chest X-ray images for efficient medical diagnosis.

Keywords

SEO-optimized keywords: COVID-19 detection, Feature extraction, Feature selection, Deep learning architecture, Image classification, Chest X-ray images, DL-based mechanisms, Respiratory conditions, Data acquisition, ALexNet, Gray-Level Co-occurrence Matrix, PCA techniques, ISSA optimization algorithm, Dataset dimensionality, Machine learning algorithms, Medical imaging analysis, Radiomics, Computer-aided diagnosis, Pattern recognition, Image processing, Data preprocessing techniques.

SEO Tags

COVID-19 detection, Deep learning, Feature extraction, Feature selection, Machine learning, Artificial intelligence, Medical imaging, Radiomics, CT scans, X-rays, Data preprocessing, Classification algorithms, Feature engineering, Image analysis, Data mining, Computer-aided diagnosis, Pattern recognition, Improved Salp Swarm Algorithm, ALexNet, Gray-Level Co-occurrence Matrix, Principal Component Analysis, Chest X-ray classification, DL network architecture.