Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2021-08, Vol.24 (3), p.1111-1124
Main Authors: Das, Amit Kumar, Ghosh, Sayantani, Thunder, Samiruddin, Dutta, Rohit, Agarwal, Sachin, Chakrabarti, Amlan
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Chest
Computer Science
Coronaviruses
COVID-19
Ensemble learning
Graphical user interface
Medical imaging
Medical personnel
Neural networks
Pattern Recognition
Theoretical Advances
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Summary:COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper, we have proposed a Deep Convolutional Neural Network-based solution which can detect the COVID-19 +ve patients using chest X-Ray images. Multiple state-of-the-art CNN models—DenseNet201, Resnet50V2 and Inceptionv3, have been adopted in the proposed work. They have been trained individually to make independent predictions. Then the models are combined, using a new method of weighted average ensembling technique, to predict a class value. To test the efficacy of the solution we have used publicly available chest X-ray images of COVID +ve and –ve cases. 538 images of COVID +ve patients and 468 images of COVID –ve patients have been divided into training, test and validation sets. The proposed approach gave a classification accuracy of 91.62% which is higher than the state-of-the-art CNN models as well the compared benchmark algorithm. We have developed a GUI-based application for public use. This application can be used on any computer by any medical personnel to detect COVID +ve patients using Chest X-Ray images within a few seconds.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-021-00970-4