COVID-19 Detection through Deep Feature Extraction

• URL: http://arxiv.org/abs/2111.10762v1
• Date: Sun, 21 Nov 2021 08:32:08 GMT
• Title: COVID-19 Detection through Deep Feature Extraction
• Authors: Jash Dalvi, Aziz Bohra
• Abstract summary: 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 achieves a cross-validation accuracy of 100% on the COVID-19 and Normal X-Ray image classes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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