ABSTRACT
In this paper, we investigate the role of the chromatic information in CT scans in COVID-19 detection and we aim to confirm the inclusion of the artificial intelligence findings in assisting COVID-19 diagnosis. This paper proposes a freezing-based convolutional neural network learning using a morphological transformation of CT images to classify COVID-19 cohorts to help in prognostication pneumonia disease monitoring. The experiments made on the collected CT images from previous works have proven to be a powerful aid to recognize the lesions in CT images which works at comprehensively greater accuracy and speed. The proposed CNN architecture has reflected the viral proliferation in infected patients and archives an accuracy of 87.56% with an improvement by 3% compared to the baseline method on the available database of CT images.
