Deep learning based detection of COVID-19 from chest X-ray images

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc. In this paper, we propose a methodology to investigate the potential of deep transfer learning in building a classifier to detect COVID-19 positive patients using CT scan and CXR images. Data augmentation technique is used to increase the size of the training dataset in order to solve overfitting and enhance generalization ability of the model. Our contribution consists of a comprehensive evaluation of a series of pre-trained deep neural networks: ResNet50, InceptionV3, VGGNet-19, and Xception, using data augmentation technique. The findings proved that deep learning is effective at detecting COVID-19 cases. From the results of the experiments it was found that by considering each modality separately, the VGGNet-19 model outperforms the other three models proposed by using the CT image dataset where it achieved 88.5% precision, 86% recall, 86.5% F1-score, and 87% accuracy while the refined Xception version gave the highest precision, recall, F1-score, and accuracy values which equal 98% using CXR images dataset. On the other hand, and by applying the average of the two modalities X-ray and CT, VGG-19 presents the best score which is 90.5% for the accuracy and the F1-score, 90.3% for the recall while the precision is 91.5%. These results enables to automatize the process of analyzing chest CT scans and X-ray images with high accuracy and can be used in cases where RT-PCR testing and materials are limited.

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“…less RAM is needed to use this framework). Despite this, it turned out to be a high-performing system (Guefrechi et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 detection in CT and CXR images using deep learning models

et al. 2022

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“…Efficient-NetB0, ResNet50, and VGG16 are used in this work as feature extractors. In another study [44], three deep learning models namely VGG16, InceptionV3, and ResNet50 are fine-tuned on the dataset for accurate classification of COVID-19. To increase the number of sample images for experiment, they also performed data augmentation techniques.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

2022

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The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.

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“…COVID-19 is one of the coronaviruses that attacks the human respiratory system and may lead to death [84][85][86]. More than two years after the spread of this virus, which is still growing daily, infecting, and destroying many lives.…”

Section: Deep Learning Vs Covid-19mentioning

confidence: 99%

Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective

Mijwil

Aggarwal

Doshi

et al. 2022

AJAS

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Today, humans live in the era of rapid growth in electronic devices that are based on artificial intelligence, including the significant growth in the manufacture of machines that perform intelligent human tasks to solve complex situations. Artificial intelligence will significantly influence the development of many domains, especially the medical domain, which relies heavily on artificial intelligence techniques in diagnosing disease data and manufacturing drugs and vaccines. Artificial intelligence has unexpectedly advanced in helping physicians and healthcare workers save many lives, especially during the spread of the COVID-19 virus. This article reviews some literature that have applied deep learning techniques to detect COVID-19 based on chest x-rays and CT-scans images. This article concluded that deep learning techniques have a fundamental and significant role in diagnosing a big dataset of images and assisting specialists in determining whether a person is infected (positive cases).

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Deep learning based detection of COVID-19 from chest X-ray images

Cited by 59 publications

References 26 publications

COVID-19 detection in CT and CXR images using deep learning models

COVID-19 detection in CT and CXR images using deep learning models

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective

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