oGoogleNet: An Optimized GoogleNet for Chest Infection Detection on the COVID-19 Dataset

Sumaira Zafar; Kanwal  Majeed; Arjmand  Majeed; Syed Farooq  Ali; Aadil Zia  Khan

Sumaira Zafar School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Kanwal Majeed School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Arjmand Majeed School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Syed Farooq Ali School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Aadil Zia Khan School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

Keywords: Deep Learning, Convolution’s Neural Networks, GoogleNet

Abstract

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The outbreak of SARS and, more recently, COVID-19 has highlighted the
need for accurate and quick diagnosis of chest diseases for pandemic prevention. While the
handling of theCOVID-19 pandemic has drawn attention to the weaknessesin the
healthcare systems worldwide, it has also enabled us to fully utilize the massive amounts
of data at our disposal in order to devise strategies for better handling outbreaks in the
future. Chest infection is a crucial symptom used to diagnose COVID-19 cases.
Moreover, it may also lead to various other diseases, including pneumonia, asthma, and
bronchitis. Researchers havebeen working on automatic chest infection detection for the
last few decades. In this study, we present oGoogleNet, a deep learning architecture for
chest infection detection, developed by optimizing GoogleNet through the addition of
layers and the modification of activation functions. The oGoogleNet iscompared with the
existing state-of-the-art deep networks on eight standard chest infection datasets,
containing 12,389 radiographs (with 777 COVID-19 radiographs). The experiments
demonstrate that oGoogleNet outperforms the other systems and achieves an accuracy of
91.25%.

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References

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