CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays. (15th November 2022)

• Record Type: Journal Article
• Authors: Dey, Subhrajit; Bhattacharya, Rajdeep; Malakar, Samir; Schwenker, Friedhelm; Sarkar, Ram
• Is Part Of: Expert systems with applications. Volume 206(2022)
• Publication Date: 2022-11-15
• Subjects: Sugeno integral -- COVID-19 -- Deep learning -- ConvLSTM -- Pneumonia -- Fuzzy ensemble; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117812
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The rapid outbreak of COVID-19 has affected the lives and livelihoods of a large part of the society. Hence, to confine the rapid spread of this virus, early detection of COVID-19 is extremely important. One of the most common ways of detecting COVID-19 is by using chest X-ray images. In the literature, it is found that most of the research activities applied convolutional neural network (CNN) models where the features generated by the last convolutional layer were directly passed to the classification models. In this paper, convolutional long short-term memory (ConvLSTM) layer is used in order to encode the spatial dependency among the feature maps obtained from the last convolutional layer of the CNN and to improve the image representational capability of the model. Additionally, the squeeze-and-excitation (SE) block, a spatial attention mechanism, is used to allocate weights to important local features. These two mechanisms are employed on three popular CNN models – VGG19, InceptionV3, and MobileNet to improve their classification strength. Finally, the Sugeno fuzzy integral based ensemble method is used on these classifiers' outputs to enhance the detection accuracy further. For experiments, three chest X-ray datasets, which are very prevalent for COVID-19 detection, are considered. For all the three datasets, it is found that the results obtained by the proposed method are comparable to state-of-the-art methods. The code, along with the pre-trained models, can be found at https://github.com/colabpro123/CovidConvLSTM . Highlights: Proposed a model to classify COVID-19, Pneumonia, and Normal chest X-ray images. Generated feature maps using three pre-trained standard deep learning models. Improved the spatial dependency among feature maps prior to use as features. Used squeeze-and-excitation block as a spatial attention scheme. Utilized the Sugeno fuzzy integral based classifier ensembling technique.