RLDD: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in IIoT

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention Most diagnostic methods for COVID-19 is based on Nucleic Acid Testing (NAT), which is expensive and time-consuming This paper investigates the feasibility of employing Computed Tomography(CT) images of lungs as the diagnostic signals Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent Through a public dataset, we propose an advanced Residual Learning Diagnosis Detection (RLDD) scheme for COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pre-training requirement on other medical datasets In the test set, we achieve an accuracy of 91 33%, a precision of 91 30% and a recall of 90% For the batch of 150 samples, the assessment time is only 4 7s Therefore, RLDD can be integrated into the application programming interface (API) and embedded into the medical instrument to improve the detection efficiency of COVID-19 IEEE