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

• Published in: Pattern analysis and applications : PAA Vol. 24; no. 3; pp. 1111 - 1124
• Main Authors: Das, Amit Kumar, Ghosh, Sayantani, Thunder, Samiruddin, Dutta, Rohit, Agarwal, Sachin, Chakrabarti, Amlan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2021; Springer Nature B.V
• 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; COVID-19; Deep learning; Coronavirus; Ensemble learning; Convolutional neural network
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-021-00970-4

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.