Paper Title
Spotting of Covid-19 Patients with Chest X-Ray Images using Adaptive Dual Stage Horse Herd Bidirectional LSTM Framework

Abstract - COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest X-ray images. Chest X-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive Dual stage horse herd bidirectional LSTM model is presented for the classification of images into normal, Lung opacity, viral pneumonia and COVID-19. Initially, the input images are pre-processed using modified histogram equalization (MHE) approach. This is utilized to improve the contrast of the images by changing low resolution images into high resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform (EDTCWT) is introduced to extract the high density features to decrease the complexity of features. Moreover, the features dimensionality is reduced by adaptive beetle antennae optimization (ABAO) algorithm is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive Dual stage horse herd bidirectional LSTM (ADHH-BiLSTM) model is utilized for the classification of images into normal, viral pneumonia, Lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), AUC (99%) for COVID-19 Chest X-ray database. Keywords - Pre-processing, Feature Extraction, Feature Selection, Optimization, Deep Learning, Classification