POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models

TL;DR

This paper compares various deep learning models using transfer learning on CT scans for COVID-19 detection, highlighting VGG-16's superior performance with 85.33% accuracy.

Contribution

It provides a comparative analysis of deep learning models for COVID-19 detection via CT scans, emphasizing the effectiveness of transfer learning techniques.

Findings

VGG-16 achieved 85.33% accuracy.

Transfer learning improves COVID-19 detection from CT scans.

VGG-16 outperforms other models in this task.

Abstract

The novel coronavirus disease (COVID-19) constitutes a public health emergency globally. It is a deadly disease which has infected more than 230 million people worldwide. Therefore, early and unswerving detection of COVID-19 is necessary. Evidence of this virus is most commonly being tested by RT-PCR test. This test is not 100% reliable as it is known to give false positives and false negatives. Other methods like X-Ray images or CT scans show the detailed imaging of lungs and have been proven more reliable. This paper compares different deep learning models used to detect COVID-19 through transfer learning technique on CT scan dataset. VGG-16 outperforms all the other models achieving an accuracy of 85.33% on the dataset.