Abstract
In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.
Highlights
Introduction to COVID19 and DiagnosisReceived: 11 November 2021The widespread COVID-19 pandemic constitutes a severe threat to global health.most new research has used tools and techniques for tracking COVID-19 and discovering various infection areas to minimize the risk of its spread
Most new research has used tools and techniques for tracking COVID-19 and discovering various infection areas to minimize the risk of its spread
Machine learning (ML) algorithms are known for learning underlying relationships ofinerror, parameters, maximum number of connections, data network and making decisions the without the need for explicit instructions.and capacity learning (ML)oralgorithms are known relationships of aMachine
Summary
The widespread COVID-19 pandemic constitutes a severe threat to global health. most new research has used tools and techniques for tracking COVID-19 and discovering various infection areas to minimize the risk of its spread. Machine learning and AI approaches can evaluate large quantities of COVID19 data to create new models and techniques for diagnosing COVID-19. AI techniques enable a global visualization of the analyzed big data of COVID-19. The visualization uses AI to present an overview of global health and confirmed cases of COVID-19. Positive X-ray results results reduce the need for CT screening if there is a strong clinical suspicion of COVID-19 reduce the need for CT screening if there is a strong clinical suspicion of COVID-19 infecinfection [11] This presents limitations for patients, including pregnant women, tion [11]. A deep learning and CT scans.sample-efficient algorithm for the diagnosis of COVID-19 based on CXR and scans.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
