A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

Abstract

The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16 , Xception and InceptionV3 , where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3 . The source code of this work is available at https://github.com/subhankar01/covidfs-aihc .

Metaheuristics based COVID-19 detection using medical images: A review

“…Several hidden layers can be added and this version of NN is called Multi-Layer Neural Network (MLNN) or Multi-Layer Perceptron (MLP). Many studies [ 9 , 12 , 13 , 18 , 22 , 27 ] have used fully connected neural networks for classification and have achieved more than 90% accuracy on test data.…”

“…Meanwhile, the reported accuracy was 99.22%. Bhowal et al [ 27 ] investigated three different deep learning architectures, namely VGG16, Xception, and InceptionV3. This work performed experiments using X-ray images from datasets 2, 5, and 22.…”

Designing Hybrid CNN-SVM Model for COVID-19 Classification Based on X-ray Images Using LGBM Feature Selection

“…Feature extraction is critical in computer vision, image processing, and pattern recognition systems. It has been utilized in various models to extract deep features [9]. As previously stated, CNN models extract and choose only the most representative features from the input image.…”

“…A vaccination program that is secure and safe would be a huge success. However, only a few effective vaccines have been discovered to the present, and it is believed that it will take an inordinate amount of time to protect the entire world against the hazards of COVID-19 [9]. Furthermore, the coronavirus evolves, impeding the development of a vaccine.…”

“…In another study, Bhowal et al [9] presented a transfer learning-based technique to extract features, VGG16, Xception, and InceptionV3, using pre-trained models. After performing feature selection, the obtained features will be sent to a multi-layer perceptron for classification.…”