COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm

• Published in: Medical & biological engineering & computing Vol. 60; no. 10; pp. 2931 - 2949
• Main Authors: Xu, Binfeng, Martín, Diego, Khishe, Mohammad, Boostani, Reza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Benchmarks; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computed tomography; Computer Applications; Coronaviruses; COVID-19; Datasets; Diagnosis; Human Physiology; Imaging; IP (Internet Protocol); Lungs; Medical diagnosis; Medical imaging; Neural networks; Original; Original Article; Polymerase chain reaction; Radiology; Sensitivity; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Trigonometric functions; Viral diseases; Viruses; Chest CT scans; COVID-19; DCNNs; Sine-cosine algorithm; Internet protocol address
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-022-02637-6

The prevalence of the COVID-19 virus and its variants has influenced all aspects of our life, and therefore, the precise diagnosis of this disease is vital. If a polymerase chain reaction test for a subject is negative, but he/she cannot easily breathe, taking a computed tomography (CT) image from his/her lung is urgently recommended. This study aims to optimize a deep convolution neural network (DCNN) structure to increase the COVID-19 diagnosis accuracy in lung CT images. This paper employs the sine-cosine algorithm (SCA) to optimize the structure of DCNN to take raw CT images and determine their status. Three improvements based on regular SCA are proposed to enhance both the accuracy and speed of the results. First, a new encoding approach is proposed based on the internet protocol (IP) address. Then, an enfeebled layer is proposed to generate a variable-length DCNN. The suggested model is examined over the COVID-CT and SARS-CoV-2 datasets. The proposed method is compared to a standard DCNN and seven variable-length models in terms of five known metrics, including sensitivity, accuracy, specificity, F1-score, precision, and receiver operative curve (ROC) and precision-recall curves. The results demonstrate that the proposed DCNN-IPSCA surpasses other benchmarks, achieving final accuracy of (98.32% and 98.01%), the sensitivity of (97.22% and 96.23%), and specificity of (96.77% and 96.44%) on the SARS-CoV-2 and COVID-CT datasets, respectively. Also, the proposed DCNN-IPSCA performs much better than the standard DCNN, with GPU and CPU training times, which are 387.69 and 63.10 times faster, respectively. Graphical abstract