COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images
This work addresses early detection of COVID-19 for medical diagnosis, but it is incremental as it combines existing methods like deep CNNs and meta-heuristic feature selection.
The paper tackled COVID-19 detection from X-ray images using a hybrid deep learning and feature selection method, achieving an accuracy of 99.43%, sensitivity of 99.16%, and specificity of 99.57% on a dataset of 1092 samples.
The new Coronavirus is spreading rapidly, and it has taken the lives of many people so far. The virus has destructive effects on the human lung, and early detection is very important. Deep Convolution neural networks are such powerful tools in classifying images. Therefore, in this paper, a hybrid approach based on a deep network is presented. Feature vectors were extracted by applying a deep convolution neural network on the images, and useful features were selected by the binary differential meta-heuristic algorithm. These optimized features were given to the SVM classifier. A database consisting of three categories of images such as COVID-19, pneumonia, and healthy included in 1092 X-ray samples was considered. The proposed method achieved an accuracy of 99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results demonstrate that the suggested approach is better than recent studies on COVID-19 detection with X-ray images.