Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Transfer Learning for the Detection and Diagnosis of Types of Pneumonia Including Pneumonia Induced by the COVID-19 from Chest X-Ray Images

Version 1 : Received: 22 July 2021 / Approved: 23 July 2021 / Online: 23 July 2021 (15:02:40 CEST)

A peer-reviewed article of this Preprint also exists.

Brima, Y.; Atemkeng, M.; Tankio Djiokap, S.; Ebiele, J.; Tchakounté, F. Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images. Diagnostics 2021, 11, 1480. Brima, Y.; Atemkeng, M.; Tankio Djiokap, S.; Ebiele, J.; Tchakounté, F. Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images. Diagnostics 2021, 11, 1480.

Journal reference: Diagnostics 2021, 11, 1480
DOI: 10.3390/diagnostics11081480

Abstract

Accurate early diagnosis of COVID-19 viral pneumonia, primarily in asymptomatic people is essential to reduce the spread of the disease, the burden on healthcare capacity, and the overall death rate. It is essential to design affordable and accessible solutions to distinguish pneumonia caused by COVID-19 from other types of pneumonia. In this work, we propose a reliable approach based on deep transfer learning that requires few computations and converges faster. Experimental results demonstrate that our proposed framework for transfer learning is a potential and effective approach to detect and diagnose types of pneumonia from chest X-ray images with a test accuracy of 94.0%.

Keywords

ANN; COVID-19; CT; mRNA; MRI; RT-PCR; SARS-CoV-2; XCR

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