COVID-19 image classification using deep features and fractional-order marine predators algorithm

Abstract

Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.

COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases

“…From their obtained results, the VGG-19 and MobileNet architectures achieved the best performance compared with the other used CNN architectures. In [ 28 ], A. T. Sahlol proposed using deep features that were extracted from the Inception architecture and a swarm-based feature selection algorithm to recognize COVID-19 infection from the X-ray scans. Their approach achieved considerable improvement compared with the set of feature selection algorithms and CNNs architectures.…”

Peer Review #2 of "COVID-19: a new deep learning computer-aided model for classification (v0.1)"

“…They used three modalities bacterial pneumonia, viral pneumonia, and normal lung diseases based on transfer learning using VGG16, Resnet50, and Inception V3 and the resulting accuracies are 93.8%, 89.2%, and 82.4% respectively. Fractional-order and marine predators algorithm (FO-MPA) with inception CNN presented by Sahlol et al (Sahlol et al, 2020) are used to extract the features and classify the COVID-19 chest X-ray images respectively. The results obtained are 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for the applied dataset-1 and dataset-2, from Kaggle website respectively.…”

Changes in physicochemical, heavy metals and air quality in life episode of Aplocheilus panchax along Mahanadi industrial belt of India as a function of social lockdowns

“…Environmentalists have been advocating that the self-regenerated nature will be degenerated quickly in post-COVID-19 timings because of the massive re-opening of the industries and vehicular exhaustion 5 . The industries will try to cover up the loss they did bear during the COVID-outbreak in post-COVID-19 time periods 36,37,38,39 . Mahanadi River system is the 3rd largest in the peninsula of India and serves as the source of domestic water supply to many cities including Sambalpur (0.2 million population), Cuttack (0.5 million population) and Paradeep (a major port of the country and 0.15 million population) besides many o cially undocumented number of rural and minor urban settlements.…”