InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Abstract

Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database

“…In the new task of COVID19 screening, it is advantageous to provide additional information to the radiologists regarding the computer decision together with the final predicted result. In most of the existing studies [39] , [40] , [41] , the conventional CAM visualization is based on a single network and presents the intermediate features of each subnetwork separately in case of an ensemble design.…”

A Comparative Study of Multiple Neural Network for Detection of Covid 19 on Chest X-Ray

“…Compared with other works that worked on binary classification of CXR images, our model has the second highest accuracy. The highest binary classification accuracy is obtained by Gupta et al [18] using their proposed network called InstaCovNet-19. In summary, our experimented model DenseNet-121 using transfer learning technique, has achieved very good performance in binary classification and has used the highest number of images among the methods mentioned above.…”

“…The classification accuracy obtained in this study is 91.7%. Gupta et al [18] proposed an integrated stacked deep convolution network called InstaCovNet-19 which makes use of InceptionV3, NASNet, Xception, MobileNetV2 and ResNet101. The proposed model achieved accuracy of 99.53% in binary (COVID vs Non-COVID) classification, and accuracy of 99.08% in 3-class (COVID-19, pneumonia, normal) classification.…”

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

“… [46] Ensemble Deep Learning Model CT 500 COVID-19 (＋), 500 COVID-19 (−) Gupta et al. [47] Integrated Stacking InstaCovNet-19 model X-ray 361 COVID-19 (＋), 365 Normal (−), 362 Pneumonia Aslan et al. [48] CNN-based transfer learning–BiLSTM X-ray 219 COVID-19 (＋), 1341 COVID-19 (−), 1345 viral pneumonia …”