COVID-19 Detection through Deep Feature Extraction

TL;DR

This paper presents a deep learning approach using ResNet50 and logistic regression to accurately detect COVID-19 from X-ray images, achieving near-perfect accuracy on the Kaggle dataset.

Contribution

It introduces a novel combination of deep feature extraction with ResNet50 and logistic regression for COVID-19 detection from radiography images.

Findings

Achieved 100% accuracy on binary classification of COVID-19 vs. Normal.

Achieved 98.84% accuracy on three-class classification.

Demonstrated effectiveness of deep features combined with simple classifiers.

Abstract

The SARS-CoV2 virus has caused a lot of tribulation to the human population. Predictive modeling that can accurately determine whether a person is infected with COVID-19 is imperative. The study proposes a novel approach that utilizes deep feature extraction technique, pre-trained ResNet50 acting as the backbone of the network, combined with Logistic Regression as the head model. The proposed model has been trained on Kaggle COVID-19 Radiography Dataset. The proposed model achieves a cross-validation accuracy of 100% on the COVID-19 and Normal X-Ray image classes. Similarly, when tested on combined three classes, the proposed model achieves 98.84% accuracy.