Unified Ensemble Learning Approach for COVID-19 Detection Using Deep EnTraCT

Problem Definition

The existing literature on FE and FS techniques for improving classification accuracy in COVID-19 detection has shown promise in enhancing the performance of models. However, the complexity of these architectures, with multiple layers, poses a significant limitation in terms of interpretability. The lack of transparency in understanding why these complex models make certain predictions can hinder the trust and validation of the results, particularly in critical applications like COVID-19 detection. This limitation underscores the need for a more interpretable and transparent approach in developing models for COVID-19 detection, where the rationale behind predictions is crucial for decision-making and further improvements in model performance. Addressing this issue is essential for ensuring the reliability and effectiveness of models in accurately detecting COVID-19 cases, thus highlighting the necessity of developing a more interpretable model in this domain.

Objective

The objective of this study is to develop a more interpretable and transparent deep learning model, Deep EnTraCT, for improving the classification accuracy in COVID-19 detection using chest X-ray images. By combining feature extraction techniques, feature selection methods, and an advanced DL architecture, the aim is to enhance the model's performance while reducing complexity. The model seeks to address the lack of interpretability in current models by selecting relevant features and utilizing ensemble learning approaches for more reliable and accurate predictions. Ultimately, the goal is to ensure the reliability and effectiveness of the model in accurately detecting COVID-19 cases and providing a rationale behind its predictions for decision-making and further advancements in model performance.

Proposed Work

To overcome the limitations of previous DL models, a new Deep EnTraCT model is presented for identifying and classifying given CXR images into three classes of normal, Covid-19 and pneumonia. Here, we are using the same FE and FS technique that was used in previous case because they improved accuracy to a good extent. Initially, AlexNet based DL-pre-trained model is used for extracting features from given CXR images to form the first feature set. After this, statistical, GLCM and PCA techniques are used for extracting textural patterns from original CXR images to create a second subset. Nevertheless, we know that all features extracted from these techniques are not relevant to covid-19 detection and may unnecessarily increase its complexity.

Therefore, ISSA optimization technique is applied on the second feature set to effectively select only relevant and informative features while discarding the redundant features. Also, PCA based feature selection technique is applied on the first feature set to select meaningful features from it and discard the irrelevant ones. By doing so, we preserve only important features and hence are able to solve dimensionality issues faced in large datasets like covid-19. The final feature list is created by combining the feature selected by ISSA and PCA feature selection techniques. Now, the main work starts wherein an advanced DL model (Deep EnTraCT) is proposed for increasing the classification accuracy rate of covid-19 detection model while reducing complexity.

The term "Deep" signifies the model's ability to delve into intricate features, while "EnTraCT" highlights its use of ensemble, transfer, and composition methods. This modified approach maintains the core principles of the original "DeepTraCTive" model while placing greater emphasis on ensemble learning. The Deep EnTraCT architecture incorporates deeper layers, batch normalization, dropout regularization, adjusted filter sizes, max pooling, and ReLU activation functions for improving the model's capacity to capture intricate image features effectively, thereby boosting its overall performance in COVID-19 detection. But what really improves the performance of the proposed Deep EnTraCT model is the introduction of EL concept in final predictions. During this phase, three separate instances of the DeTraC model are created and trained independently to add diversity in solutions.

The predictions made by three models are then combined by using the voting mechanism that aids in mitigating bias and variance, ultimately resulting in enhanced classification accuracy.

Application Area for Industry

This project can be applied in various industrial sectors such as healthcare, pharmaceuticals, and biotechnology for improving the accuracy and efficiency of COVID-19 detection using chest X-ray images. The proposed Deep EnTraCT model addresses the challenge of interpretability in complex deep learning architectures by utilizing feature extraction and selection techniques to reduce complexity and enhance relevant feature selection. By combining ensemble learning, transfer learning, and composition methods, the model improves classification accuracy while maintaining a focus on trust, validation, and performance improvement in critical applications like COVID-19 detection. The benefits of implementing these solutions include increased accuracy rates, reduced dimensionality issues in large datasets, and the ability to provide interpretable predictions for better decision-making in industries where understanding the rationale behind predictions is crucial.

Application Area for Academics

The proposed project on Deep EnTraCT model has the potential to enrich academic research, education, and training in the field of deep learning and medical image analysis. By addressing the limitations of previous DL models in COVID-19 detection, this project introduces innovative techniques such as ISSA optimization, PCA feature selection, and ensemble learning to improve classification accuracy rates while reducing complexity. Researchers in the specific domain of medical image analysis can utilize the code and literature of this project to enhance their understanding of DL models and explore new methodologies for image classification. MTech students and PhD scholars can benefit from this project by incorporating the Deep EnTraCT architecture into their research work, enabling them to delve deeper into intricate image features and improve their model's performance. Furthermore, this project opens up avenues for exploring innovative research methods, simulations, and data analysis within educational settings.

By incorporating advanced DL techniques like ISSA optimization and ensemble learning, educators can provide students with hands-on experience in developing solutions for real-world problems like COVID-19 detection. This project's relevance lies in its potential to revolutionize the field of medical image analysis and inspire future research in deep learning applications for healthcare. In conclusion, the proposed Deep EnTraCT model offers a comprehensive framework for improving the accuracy of COVID-19 detection models. Its innovative approach to feature extraction and ensemble learning can significantly contribute to academic research, education, and training in the field of deep learning and medical image analysis. The project's future scope includes exploring the application of the Deep EnTraCT architecture in other medical imaging tasks and expanding its capabilities to address a wider range of healthcare challenges.

Algorithms Used

ISSA optimization technique is used for feature selection by effectively selecting relevant and informative features while discarding the redundant ones. PCA technique is applied for feature selection to solve dimensionality issues in large datasets. The proposed Deep EnTraCT model incorporates ensemble learning, deeper layers, batch normalization, dropout regularization, adjusted filter sizes, max pooling, and ReLU activation functions to improve the model's capacity to capture intricate image features effectively, thereby enhancing overall performance in COVID-19 detection. The use of ensemble learning and the introduction of the EL concept in final predictions further boost the classification accuracy of the model.

Keywords

SEO-optimized keywords: FE technique, FS technique, DeTraC model, deep learning, COVID-19 detection, interpretability, classification accuracy, Deep EnTraCT model, CXR images, AlexNet, feature extraction, statistical techniques, GLCM, PCA, ISSA optimization, feature selection, ensemble learning, batch normalization, dropout regularization, ReLU activation, EL concept, voting mechanism, disease classification, medical imaging, pneumonia detection, radiology, computer-aided diagnosis, convolutional neural networks, image-based diagnosis.

SEO Tags

covid-19 classification, deep learning, deep neural networks, medical image analysis, computer-aided diagnosis, chest x-ray images, image classification, convolutional neural networks, COVID-19 detection, COVID-19 screening, disease classification, image-based diagnosis, pneumonia detection, radiology, ensemble learning, transfer learning, feature selection, feature extraction, machine learning optimization, ISSA optimization, PCA techniques, DeTraC model, Deep EnTraCT model, COVID-19 prediction, predictive modeling, voting mechanism, model performance, research scholar, PHD student, MTech student.