A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images. (27th December 2021)

• Record Type: Journal Article
• Authors: Syed, Hassaan Haider; Khan, Muhammad Attique; Tariq, Usman; Armghan, Ammar; Alenezi, Fayadh; Khan, Junaid Ali; Rho, Seungmin; Kadry, Seifedine; Rajinikanth, Venkatesan
• Other Names: Picconi Barbara Academic Editor.
• Is Part Of: Behavioural neurology. Volume 2021(2021)
• Publication Date: 2021-12-27
• Subjects: Neuropsychology -- Periodicals; Neuropsychiatry -- Periodicals; Cognitive neuroscience -- Periodicals; 616.8005
• DOI: 10.1155/2021/2560388
• Languages: English
• ISSNs: 0953-4180

Abstract

Abstract : The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).