COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.
【초록키워드】 COVID-19, Treatment, pandemic, deep learning, Pneumonia, quarantine, Tuberculosis, X-ray, early diagnosis, COVID-19 infection, comparison, Early detection, dataset, patients, strength, researcher, overfitting, tested, affected, healthy, overcome, 【제목키워드】 COVID-19, Screening, Model, Image, deep,