# Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks

Source: [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa3c2c1e209131ce8d8bea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T01:00:54.683Z

Authors: Ioannis D. Apostolopoulos, Tzani Bessiana

Published: 2020-03-25T20:34:30.000Z

## Abstract

In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this experiment is a collection of 1427 X-Ray images. 224 images with confirmed Covid-19, 700 images with confirmed common pneumonia, and 504 images of normal conditions are included. The data was collected from the available X-Ray images on public medical repositories. With transfer learning, an overall accuracy of 97.82% in the detection of Covid-19 is achieved.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.11617>)

[PDF](<https://arxiv.org/pdf/2003.11617.pdf>)