A Novel Machine Learning Framework for Covid-19 Image Classification with Bio-heuristic Optimization

ABSTRACT

Due to its rapidly advancing spread, the world is still reeling from COVID-19 (coronavirus 2019), which is categorized as a highly infectious disease. An early diagnosis is very critical in treating COVID-19 patients due to its lethal implications. However, the shortage of X-ray machines has resulted in life-threatening conditions and delays in diagnosis, increasing the number of deaths around the world. Therefore, in order to avoid such fatalities, COVID-19 has to be detected earlier and diagnosed faster using an intelligent computer-aided diagnosis system than with traditional screening programs. We present a novel framework for COVID-19 image categorization in this article that utilizes deep learning (DL) and bio-inspired optimization techniques. A bio-heuristic optimizer algorithm MoFAL is utilized as a feature selector to decrease the dimensionality of the image representation and increase the accuracy of the classification by ensuring that only the most essential selected features are used. Furthermore, the feature extraction is realized using the MobileNetV3 DL model. The experimental results deduced indicate that our proposed approach drastically improves performance in terms of classification accuracy and reduction in dimensions reflected during the period of feature extraction and its phases of selection. We propose that our COVID-Classifier can be deployed in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.