ArticleSN computer science2022
A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification.
Article in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Pneumonia detection with QCSA network on chest X-ray.Scientific reports · 2023Article
- COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.Multimedia tools and applications · 2023Article
- A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with Diabetes.Frontiers in public health · 2023Article
- Machine learning algorithms in microbial classification: a comparative analysis.Frontiers in artificial intelligence · 2023Review
- COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images.Sensors (Basel, Switzerland) · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.SN computer science · 2022Article
- Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images.SN computer science · 2022Article
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3 authors.
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Abstract
The COVID-19 pandemic creates a significant impact on everyone's life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.
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