PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is harnessed.The EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVID-19 patient classification but also underscores the potency of ensemble-based feature selection in conjunction with the XGBoost classifier as a formidable tool in the realm of medical diagnosis and classification.

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Graph Neural Network and Its Applications

Biswas

2023

Concepts and Techniques of Graph Neural Networks

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Graph neural network (GNN) is an emerging field in deep learning. Graphs have more expressive power than any other data structure. Graph neural network is one of the application areas of deep learning, and it has applications in different domains where traditional convolutional neural networks can't give the desired result. Graphs are basically connections of nodes through the edges. In the area of recommendation systems, image processing and fraud detection are some of the few application areas of graph neural networks. As graphs are moveable and mobile in nature, they are more flexible to apply in these domains. GNN deals with these types of problems more effectively than a convolution neural network. To apply GNN to a specific problem domain, data needs to be converted into a graphical format, and then neural network operations can be executed. The main feature of GNN is to inherit information from its neighborhood. This is called graph embedding. This chapter describes basic GNN architecture, GNN advantage over CNN, and its application in different domains.

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PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images

Cited by 4 publications

References 47 publications

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

Graph Neural Network and Its Applications

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