Name
Abstract | ||
|---|---|---|
The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in Xray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVIDRENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist's authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5-10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compbiomed.2021.104816 | COMPUTERS IN BIOLOGY AND MEDICINE |
Keywords | DocType | Volume |
COVID-19, X-ray, Transfer learning, Hybrid learning, Convolutional neural network, Deep learning and SVM | Journal | 137 |
ISSN | Citations | PageRank |
0010-4825 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saddam Hussain Khan | 1 | 0 | 0.68 |
| Anabia Sohail | 2 | 0 | 0.34 |
| khan | 3 | 623 | 44.09 |
| Mehdi Hassan | 4 | 57 | 6.11 |
| Yeon Soo Lee | 5 | 29 | 3.71 |
| Jamshed Alam | 6 | 0 | 0.34 |
| Abdul Basit | 7 | 30 | 16.18 |
| Saima Zubair | 8 | 0 | 0.34 |