Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models

Abstract

Introduction. While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing. Method. Inspired by the COVID-19 diagnosis methods, the latest and most efficient deep learning algorithms in the field of extracting X-ray and CT scan image features were used to identify COVID-19 in the early stages of the disease. Results. We presented a general framework consisting of two models which are developed by convolutional neural network (CNN) using the concept of transfer learning and parameter optimization. The proposed phase of the framework was evaluated on the test dataset and yielded remarkable results and achieved a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, for the first phase and 0.997, 0.9976, and 0.997 for the second phase, respectively. In all cases, the whole framework was able to successfully classify COVID-19 and non-COVID-19 cases from CT scans and X-ray images. Conclusion. Since the proposed framework was based on two deep learning models that used two radiology modalities, it was able to significantly assist radiologists in detecting COVID-19 in the early stages. The use of models with this feature can be considered as a powerful and reliable tool, compared to the previous models used in the past pandemics.

Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

“…In ref. [97], A DL algorithm based on a partitioned CNN for an automatic cancer detection system is proposed and developed. Researchers match the performance of the designed deep CNN with other conventional classification methods such as SVM and DBN.…”

COVID-19 Data Analytics Using Extended Convolutional Technique

“…The DL (deep learning) method [ 12 ] has been projected and has efficaciously received satisfying outcomes in phrases of accuracy in diverse arenas [ 6 , 12 ]. The instance research of COVID-19 examination of CT scans had been offered with the aid of using authors together with Li et al [ 13 ], Xu et al [ 14 ], Gozes et al [ 15 ], and Shi et al [ 16 ]. Authors [ 14 ] mentioned as follows as the COVID-19 eminent shows its traits which can change starting with different varieties of virus-related pneumonitis, such as biological influenza-A pneumonia [ 5 , 17 , 18 ].…”

A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI)

“…These AI algorithms outperform traditional radiological methods, offering a more accurate diagnostic tool. The Ghaderzadeh M. et al study introduced a framework comprising two models based on convolutional neural networks (CNNs) leveraging transfer learning and parameter optimization [18]. The initial phase of this framework was tested and demonstrated outstanding results, achieving a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, respectively.…”