COVID‐19 detection using hybrid deep learning model in chest x‐rays images

• Published in: Concurrency and Computation: Practice and Experience Vol. 34; no. 5
• Main Authors: Mahajan, Shubham, Raina, Akshay, Gao, Xiao‐Zhi, Pandit, Amit Kant
• Format: Journal Article Web Resource
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 28.02.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Chest; COVID-19; Deep learning; Economic analysis; Machine learning; medical images; Medical imaging; Medical research; object detection; Radiology; Viral diseases
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6747

The novel‐corona‐virus is presently accountable for 547,782 deaths worldwide. It was first observed in China in late 2019 and, the increase in number of its affected cases seriously disturbed almost every nation in terms of its economical, structural, educational growth. Furthermore, with the advancement of data‐analytics and machine learning towards enhanced diagnostic tools for the infection, the growth rate in the affected patients has reduced considerably, thereby making it critical for AI researchers and experts from medical radiology to put more efforts in this side. In this regard, we present a controlled study which provides analysis of various potential possibilities in terms of detection models/algorithms for COVID‐19 detection from radiology‐based images like chest x‐rays. We provide a rigorous comparison between the VGG16, VGG19, Residual Network, Dark‐Net as the foundational network with the Single Shot MultiBox Detector (SSD) for predictions. With some preprocessing techniques specific to the task like CLAHE, this study shows the potential of the methodology relative to the existing techniques. The highest of all precision and recall were achieved with DenseNet201 + SSD512 as 93.01 and 94.98 respectively.