Article: Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images.
Arabian journal for science and engineering
2021 Volume 47, Issue 2, Page(s) 1675–1692
Abstract: ... evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and ... segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework ... and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray ...
| Abstract | The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization. |
|---|---|
| Language | English |
| Publishing date | 2021-08-11 |
| Publishing country | Germany |
| Document type | Journal Article |
| ISSN | 2193-567X |
| ISSN | 2193-567X |
| DOI | 10.1007/s13369-021-05958-0 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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