A Deep Learning Approach for Recognizing Covid-19 from Chest X-ray using Modified CNN-BiLSTM with M-SVM

ABSTRACT

At present, Covid-19 is posing serious intimidation to students, doctors, scientists and governments all around the world. It is a single-stranded RNA virus with one of the enormous RNA genomes, and it is constantly changing through mutation. Sometimes this mutation results in a new variant. Research showed that people who come in touch with this virus mostly they are infected with lung illness. So, recognizing Covid-19 from a Chest X-ray is one of the best imaging techniques. But another issue arises when it shows that other diseases like viral pneumonia, lung opacity are also had common symptoms like as Covid-19 and these problems also can be detected from chest X-ray images. So, in this research, we proposed a deep learning approach combining Modified Convolutional Neural Network (M-CNN) and Bidirectional LSTM (BiLSTM) with an Multi-Support Vector Machine (M-SVM) classifier for detecting Covid-19, Viral Pneumonia, Lung-Opacity and normal chest. We used the COVID-19-Radiography-Dataset to assess the results of our proposed system and compared the result with some other existing systems which show our proposed system is better than others. The accuracy of classification using the proposed method is 98.67%. © 2022 IEEE.