A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging

• URL: http://arxiv.org/abs/2210.09403v1
• Date: Mon, 17 Oct 2022 20:08:41 GMT
• Title: A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging
• Authors: Gargi Desai, Nelly Elsayed, Zag Elsayed, Murat Ozer
• Abstract summary: The world is still overwhelmed by the spread of the COVID-19 virus. With over 250 Million infected cases as of November 2021 and affecting 219 countries and territories, the world remains in the pandemic period. This research aims to propose a deep learning-based approach to classify COVID-19 pneumonia patients, bacterial pneumonia, viral pneumonia, and healthy (normal cases) The proposed model has been intentionally simplified to reduce the implementation cost so that it can be easily implemented and used in different geographical areas, especially rural and developing regions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The world is still overwhelmed by the spread of the COVID-19 virus. With over 250 Million infected cases as of November 2021 and affecting 219 countries and territories, the world remains in the pandemic period. Detecting COVID-19 using the deep learning method on CT scan images can play a vital role in assisting medical professionals and decision authorities in controlling the spread of the disease and providing essential support for patients. The convolution neural network is widely used in the field of large-scale image recognition. The current method of RT-PCR to diagnose COVID-19 is time-consuming and universally limited. This research aims to propose a deep learning-based approach to classify COVID-19 pneumonia patients, bacterial pneumonia, viral pneumonia, and healthy (normal cases). This paper used deep transfer learning to classify the data via Inception-ResNet-V2 neural network architecture. The proposed model has been intentionally simplified to reduce the implementation cost so that it can be easily implemented and used in different geographical areas, especially rural and developing regions.

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