CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images

The novel Coronavirus Disease 2019 (COVID-19), originated in Wuhan (China), is a global pandemic disease. COVID-19 is an acute resolved disease, but it can also be deadly, with a 2.0 % case fatality rate. COVID-19 has abruptly and undoubtedly changed the world at the end of the second decade of the 21st century. COVID-19 is extremely contagious and quickly spreading disease globally making its early diagnosis of paramount importance. COVID-19 is perhaps the greatest challenge of mankind in the twenty-first century. The development of the disease, its transmission, and the increased mortality in several countries, make it imperative to develop a treatment, but also to protect health care and society from the transmission of the disease. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The early and automatic diagnosis of COVID-19 may be beneficial for countries for timely referral of the patient to quarantine, rapid intubation of serious cases in specialized hospitals, and monitoring of the spread of the disease. Therefore, remote control of the disease, including diagnosis, early quarantine, and follow-up, is essential. The common type of COVID-19 diagnosis test requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images reveal specific manifestations associated with this disease, so Artificial intelligence can contribute to the above perspectives.

To recognize the Covid-19 accurately, we proposed an end-to-end multi-scale-multi-encoder ensemble model, where we have aggregated the outputs from two different encoders and their different scales to obtain the final prediction probabilities. We evaluate our model's performance on three different publicly available datasets and compare them with the state-of-the-art methods. The results are encouraging and demonstrate the effectiveness of deep learning, and more specifically, transfer learning with CNNs to the automatic detection of abnormal X-ray and CT images from different datasets, related to the Covid-19 disease. However, the graphical abstract of this research is depicted in the following figure.

As COVID is a new pandemic, a huge number of positive coronavirus images are not available yet. We have collected images from different open sources, such as such as Kaggle, GitHub, and MICCAI grand challenge. The distribution of all the three datasets is presented in the following table. With the limited datasets, we apply geometry-based image augmentations and transfer learning on ImageNet.

Different studies	Class categories	Number of Images	Source References with links	Modality
	NOR	5,856	CXRI
CXR-Single-CL2	NCP	500	CIDC	X-ray
	NOR	7,864	CXRI, ChestX-ray8
CXR-Multiple-CL2	NCP	4,015	CCXRI, CIDC, BIMCV	X-ray
	NOR (Train/Test)	6,958/1,227	CheXpert+CXRI/ChestX-ray8
CXR-Individual-CL2	NCP (Train/Test)	3,515/500	CCXRI+BIMCV/CIDC	X-ray
	NOR	1,227	SCoV
CT-Single-CL2-I	NCP	1,252	SCoV	CT
	NOR	397	MGC
CT-Single-CL2-II	NCP	349	MGC	CT
	NOR	7,864	SCoV, CCII, MGC
CT-Multiple-CL2	NCP	4,015	SCoV, CCII, MGC	CT
	NOR (Train/Test)	16,616/1,227	MGC+CCII+iCTCF/SCoV
CT-Individual-CL2	NCP (Train/Test)	6,427/1,252	MGC+CCII+iCTCF/SCoV	CT
	NOR	1,583	CXRI
	CPN	4,273	CXRI
CXR-Single-CL3	NCP	500	CXRI	X-ray
	NOR	3,591	CXRI, ChestX-ray8
	CPN	4,595	CXRI, ChestX-ray8
CXR-Multiple-CL3	NCP	4,015	CCXRI, CIDC, BIMCV	X-ray
	NOR	3,591	CXRI , ChestX-ray8
	CPB	2,780	CXRI
	CPV	1,493	CXRI
CXR-Multiple-CL4	NCP	4,015	CXRI, CIDC, BIMCV	X-ray

The more details of the proposed framework can be found here:
https://arxiv.org/abs/2007.11993

All the results reported in the literature were produced using the following version Python and Python API:

• Python 3.6.5
• numpy 1.19.0
• pandas 1.0.3
• matplotlib 3.1.3
• Scikit-learn 0.22.1
• scipy 1.4.1
• keras 2.3.1

Written by
Md. Kamrul Hasan
M.Sc. in Medical Imaging and Applications (MAIA)(https://maiamaster.udg.edu/ )
Assistant Professor
Department of Electrical and Electronic Engineering (EEE)
Khulna University of Engineering & Technology (KUET)
Khulna-9203, Bangladesh
E-mail: kamruleeekuet@gmail.com or m.k.hasan@eee.kuet.ac.bd
G.Scholar: https://scholar.google.com/citations?user=36WXELIAAAAJ&hl=en

Md. Ashraful Alam
Undergraduate student
Department of Electrical and Electronic Engineering (EEE)
Khulna University of Engineering & Technology (KUET)
Khulna-9203, Bangladesh