COVID-19 detection in chest X-ray images using deep boosted hybrid learning. (October 2021)

• Record Type: Journal Article
• Authors: Khan, Saddam Hussain; Sohail, Anabia; Khan, Asifullah; Hassan, Mehdi; Lee, Yeon Soo; Alam, Jamshed; Basit, Abdul; Zubair, Saima
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: COVID-19 -- X-ray -- Transfer learning -- Hybrid learning -- Convolutional neural network -- Deep learning and SVM; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104816
• Languages: English
• ISSNs: 0010-4825

Abstract

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 X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-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. Graphical abstract: Image 1 Highlights: Two new frameworks, named as DHL and DBHL are proposed for COVID-19 detection in chest X-ray images. DHL framework exploits the learning capacity of the developed COVID-RENets and SVM. COVID-RENet systematically learns the region homogeneity and boundaries features. In the DBHL framework, rich information boosted representation is obtained by concatenating the feature space of the COVID-RENets. The proposed frameworks significantly decrease false negatives as compared to existing deep CNNs. A web predictor is developed for assisting the radiologist in making accurate COVID-19 decisions.