Hybrid Feature Extraction and Optimization Techniques for Enhanced COVID-19 Detection using CNN-based Model

Problem Definition

Covid-19, a highly contagious and deadly disease, has caused a global health crisis unlike any other. In order to effectively combat its spread and impact on society, rapid and accurate detection methods are paramount. Several researchers have explored the use of artificial intelligence, specifically deep learning models, to differentiate between Covid-19 and other similar respiratory illnesses such as Pneumonia. While these DL models have shown promise in terms of accuracy, they still face challenges that limit their effectiveness. One major issue is the complexity of the detection systems, which struggle with the high dimensionality of image data from chest X-rays.

Additionally, the variability and overlapping features present in these images further complicate the detection process, leading to lower accuracy rates. As such, there is a clear need for the development of more precise and robust detection techniques in order to better identify and differentiate Covid-19 from other respiratory diseases.

Objective

The objective of this work is to develop a more precise and robust detection technique for identifying and differentiating Covid-19 from other respiratory diseases by utilizing a dual FE technique and optimization based FS technique along with a DL architecture. The goal is to overcome complexity issues, feature redundancy, and high dimensionality problems in the detection process. By combining features extracted from CXR images using different techniques and selecting relevant and informative features through optimization algorithms, the proposed approach aims to improve the accuracy, precision, specificity, sensitivity, and F1-Score of the classification model compared to traditional methods.

Proposed Work

In order to overcome the limitations of conventional DL models, we present a new and improved Covid-19 detection model wherein a dual FE technique and optimization based FS technique is used along with a DL architecture for classifying given CXR images into three classes of normal, covid-19 infected and pneumonic respectively. During the FE phase, a pre-trained DL based AlexNet model is used having 5 convolutional layers, 3 max-pooling layers, 2 normalization and fully connected layers and 1 SoftMax layers for detecting and capturing visual patterns and structures in CXR images. Moreover, by utilizing its learned representation high-level features that are specifically related to covid-19 are extracted to form the first feature set. Next, a second feature set is formed by extracting statistical, GLCM and PCA coefficient features from original CXR images. The reason for implementing statistical and GLCM FE techniques is that they aid in determining the textural patterns in the image which helps in determining the disease.

Also, PCA is used for creating a third feature sub-set called as PCA coefficient features that depict projections of the original image on principal components and addressing dimensionality issues. The features obtained through statistical, GLCM, and PCA are then combined to form the second feature set. As we have used dual FE technique in this project for extracting meaningful features from CXR images, but it may lead to complexity issues because of the increased dimensionality feature space. Also, extracting too many features adds redundancy to the model, making it more computationally complex and that too without adding any additional discriminatory power. To solve these issues, ISSA (Improved Salp Swarm Optimization Algorithm) is implemented on the second feature set for selecting relevant and informative features.

Similarly, PCA feature selection method is implemented on the first feature set for choosing features having more impact on disease classification. This helps us in mitigating the high dimensionality issues while also removing feature redundancy and improving the computational process of the model. The final feature set is formed by combining features selected through ISSA and PCA FS techniques. Finally, a modified DeTraC model is used in the classification phase of this work. The model is modified with the incorporation of 3 convolutional layers and multiple channels (8, 16, and 32), that depict the depth of data.

By increasing the channel size, we are able to capture intricate and fine-grained features from given CXR images thereby improving its representational capacity. Additionally, other layers like batch normalization, Relu and max-pooling layers are added in the DL architecture for detecting and categorizing the given CXR image into normal, covid-19, and pneumonic respectively. Based on this, results are obtained in terms of accuracy, precision, Specificity, Sensitivity, and F1-Score, that clearly shows the supremacy of the proposed approach over traditional models.

Application Area for Industry

This project can be applied across various industrial sectors where quick and accurate disease detection is crucial, such as healthcare, pharmaceuticals, and biotechnology. In the healthcare sector, the proposed solutions can help in early identification of infectious diseases like Covid-19, leading to timely treatment and containment of outbreaks. In pharmaceuticals and biotechnology, the project can assist in drug development and testing by providing precise diagnostic tools for assessing the efficacy of treatments on patients. The challenges that industries face, such as the complexity of disease detection systems, reduced accuracy rates, and handling high-dimensional image data, can be effectively addressed by the dual FE technique and optimization-based FS technique proposed in this project. Implementing these solutions can enhance the accuracy and efficiency of disease detection processes, ultimately improving patient care and streamlining healthcare operations.

Overall, the benefits of implementing these solutions include increased accuracy rates, reduced complexity, and improved computational efficiency, making them valuable tools for various industrial applications.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of medical imaging analysis and artificial intelligence. By tackling the challenge of accurately detecting Covid-19 from chest X-ray images, the project can contribute to the development of innovative research methods and techniques for disease diagnosis. The dual feature extraction approach, incorporating pre-trained DL models like AlexNet and optimization-based feature selection techniques like ISSA and PCA, presents a novel methodology for enhancing the accuracy and efficiency of detection systems. This project holds relevance in the domain of medical image analysis and machine learning, providing a practical application for researchers, MTech students, and PhD scholars interested in exploring advanced AI methods for healthcare diagnostics. The code and literature produced in this project can serve as a valuable resource for researchers looking to improve disease detection models using deep learning architectures and feature engineering techniques.

Moreover, the incorporation of cutting-edge technologies like CNNs and advanced feature selection algorithms opens up possibilities for expanding research in the field of computer-aided diagnosis and healthcare analytics. By leveraging the strengths of DL models and optimization techniques, the project demonstrates the potential for achieving higher accuracy rates in disease classification tasks, ultimately leading to more effective healthcare solutions. In terms of future scope, the project could be extended to explore the application of similar methodologies in other medical imaging tasks or expand the classification framework to include additional diseases or conditions. Further research could focus on optimizing the DL architecture, fine-tuning the feature extraction processes, or integrating other advanced algorithms to enhance the overall performance of the detection model. This project sets the stage for continued innovation and advancement in medical imaging analysis, offering a promising pathway for future research endeavors.

Algorithms Used

In the project, a dual feature extraction (FE) technique is used to extract features from chest X-ray (CXR) images for Covid-19 detection. The first feature set is obtained using a pre-trained AlexNet model, capturing high-level visual patterns related to Covid-19. The second feature set is created using statistical, GLCM, and PCA coefficient features to represent textural patterns and reduce dimensionality. ISSA (Improved Salp Swarm Optimization Algorithm) is applied to the second feature set to select relevant and informative features while PCA feature selection is used on the first feature set to enhance disease classification impact and reduce redundancy. This helps in overcoming high dimensionality issues and improves computational efficiency.

The final feature set is formed by combining features selected through ISSA and PCA feature selection techniques. The modified DeTraC model is then used for classification, incorporating additional convolutional layers and channels to enhance feature representation and capture fine-grained details in CXR images. By utilizing these algorithms, the project aims to improve accuracy, precision, specificity, sensitivity, and F1-Score in Covid-19 detection compared to traditional models, highlighting the effectiveness of the proposed approach.

Keywords

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SEO Tags

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