COVID-19 infection detection through initial lesion classification provides early diagnosis and prevents breathing difficulties. Detecting the infectious part of the lungs using computerized tomography (CT) images has become an instantaneous detection method. In this article, a Hybrid Classification Optimization (HCO) using Recurrent Leaning and Fuzzy (RLF) is proposed. The neural network classifies infected and non-infected regions using pixel distributions and their variations. This is performed by identifying missing features and training the recurrent network using regional differences. Based on the feature availability, recurrent learning classifies the region through the input dataset and recurrent training correlation. The fuzzy predicts missing features through substituted derivatives obtained between wide ranges of variations. In this process, the maximum fuzzy derivatives for feature substitution are used for infected region prediction. The least fuzzy derivatives are prevented from the training layer to reduce false rates in region classification. This joint process improves the training consistency to leverage the detection and region classification accuracies. The proposed HCO-RLF improves detection and classification accuracy, and precision by 11.96 %, 9.98 %, and 13.42 % for the varying classification rates. Besides, the results are obtained in comparison with the existing DR-MIL, DSAE, and BS-FSA methods discussed later in the article.
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