BIO-INSPIRED NEURO-ANALYSIS FRAMEWORK FOR COVID-19 ANALYSIS THROUGH CROSS-CLASS MODELING AND PSEUDO MEDICATION SUGGESTION

Abstract

Abstract: The deadly coronavirus has not just devastated the lives of millions but has put the entire healthcare system under tremendous pressure. Early diagnosis of COVID-19 plays a significant role in isolating the positive cases and preventing the further spread of the disease. This paper proposes a novel multi-domain feature extraction method and bio-inspired Neuro-analysis to classify determined features from the Lung CT images. Our Feature Extraction method is named StaTexCNN, which compresses statistical, texture, and deep learning features. By integrating multiple elements, we can achieve the discriminate features of complicated identification of Lung tissues in CT images. Accuracy can be improved by using an ANN pattern recognition network for classification. We included the medicine suggestion from the global medicine database via Pseudo-selection retrieval. The proposed algorithm is evaluated using simulation and analyzing the algorithm with critical metrics of Accuracy, Precision, Recall, FPR, F1-Score, and Processing Time.