COVID-19 Detection through Deep Feature Extraction
This work addresses the problem of accurate COVID-19 diagnosis for medical applications, but it is incremental as it combines existing methods on a specific dataset.
The study tackled COVID-19 detection from X-ray images using a deep feature extraction approach with ResNet50 and Logistic Regression, achieving 100% accuracy on binary classification and 98.84% on three-class classification.
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.