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arXiv · 2111.10762

COVID-19 Detection through Deep Feature Extraction

Jash Dalvi·Aziz Bohra
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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.

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Jash Dalvi, Aziz Bohra. 2021-11-21. COVID-19 Detection through Deep Feature Extraction. https://arxiv.org/abs/2111.10762

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