Opening book details…
Can I read CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays on EtoBox?
CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays by Subhrajit Dey; Rajdeep Bhattacharya; Samir Malakar; Friedhelm Schwenker; Ram Sarkar is a Computer Science article available to read on EtoBox.
What is CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays about?
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. Fo
Who reads CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Subhrajit Dey; Rajdeep Bhattacharya; Samir Malakar; Friedhelm Schwenker; Ram Sarkar
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)