Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. Though, some vaccines have now been authorized worldwide, it will take huge time to reach everyone, especially in developing countries. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using coalition game theory, information theory, and Lambda fuzzy approximation. Three different sets of fuzzy measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures, three Choquet integrals are calculated and their decisions are finally combined. Besides, we have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. Our experimental results outperform many recently proposed methods. Source code of this work is available at https://github.com/ subhankar01/Covid-Chestxray-lambda-fuzzy

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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 .

This paper studies the high complexity of the calculation of fuzzy measures which can be used in fuzzy integrals to combine the decisions of different learning algorithms. To this end, this paper proposes an alternative low complexity method for the calculation of fuzzy measures that have been applied to Choquet integral for the fusion of deep learning models across different application domains for increasing the accuracy of the overall model. The paper shows that the Dempster-Shafer (DS) belief structure provides partial information about the fuzzy measures associated with a variable, and the paper devises a method to use this partial information for the calculation of fuzzy measures. An infinite number of fuzzy measures is associated with the DS belief structure. This paper proposes a theorem to calculate the general form of a specific set of fuzzy measures associated with the DS belief structure. This specific set of fuzzy measures can be expressed as a weighted summation of the basic assignment function of the DS belief structure. The main advantage of expressing the fuzzy measures in this format is that the monotonic condition which needs to be maintained during the calculation of the fuzzy measure can be avoided and only the basic assignment function needs to be evaluated. The calculation of the basic assignment function is formulated using a method inspired by the Monte Carlo approach used to calculate Value Functions in Markov Decision Process.