mlsanityData to 7 Oct 2026

COVID-19 Detection through Deep Feature Extraction

arXiv 2111.10762 ↗First posted 21 Nov 2021eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 21 Nov 2021.

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9 of 14 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Recent Advances in Convolutional Neural Networkscited by 360
  3. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  4. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200
  5. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  6. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
  7. 2020COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographscited by 28
  8. 2020A Light CNN for detecting COVID-19 from CT scans of the chestcited by 16
  9. 2020Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learningcited by 6

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