COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

Haghanifar, Arman; Majdabadi, Mahdiyar Molahasani; Choi, Youn‐Hee; S., Deivalakshmi; Ko, Seok‐Bum

doi:10.48550/arxiv.2006.13807

Cited by 11 publications

(12 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the new COVID-19 Omicron strain’s high transmission rates and vaccine-related resistance, there is an extra layer of concern. The gold standard for diagnosing COVID-19 infection is now Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR) [ 89 , 90 ]. Throughout the epidemic, the researcher advocated other technologies including as chest X-rays and Computed Tomography (CT) combined with Machine Learning and Artificial Intelligence to aid in the early detection of people who might be infected.…”

Section: Machine Learning Techniques For Different Disease Diagnosismentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

View full text Add to dashboard Cite

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

show abstract

Section: Machine Learning Techniques For Different Disease Diagnosismentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

View full text Add to dashboard Cite

show abstract

“…A web application Chester [6] was introduced for disease prediction using CXR. Haghanifar et al [42] proposed a network named COVID-CXNet also based on DenseNet-121, but they focused on distinguishing between COVID-19 and normal lung CXR images as well as a three-class problem: COVID pneumonia, non-COVID pneumonia, and normal.…”

Section: Related Workmentioning

confidence: 99%

Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System

Bose¹,

Anirvan²

2021

Preprint

View full text Add to dashboard Cite

As of June 2021, the World Health Organization (WHO) has reported 171.7 million confirmed cases including 3,698,621 deaths from COVID-19. Detecting COVID-19 and other lung diseases from Chest X-Ray (CXR) images can be very effective for emergency diagnosis and treatment as CXR is fast and cheap. The objective of this study is to develop a system capable of detecting COVID-19 along with 14 other lung diseases from CXRs in a fair and unbiased manner. The proposed system consists of a CXR image selection technique and a deep learning based model to classify 15 diseases including COVID-19. The proposed CXR selection technique aims to retain the maximum variation uniformly and eliminate poor quality CXRs with the goal of reducing the training dataset size without compromising classifier accuracy. More importantly, it reduces the often hidden bias and unfairness in decision making. The proposed solution exhibits a promising COVID-19 detection scheme in a more realistic situation than most existing studies as it deals with 15 lung diseases together. We hope the proposed method will have wider adoption in medical image classification and other related fields.

show abstract

“…This research group has achieved the best performances from VGG19 with an overall accuracy of 90% and F1-score of 90. [46][47][48][49][50]. Other researchers have also put an effort to detect COVID-19 patient from CT scans in [51,52].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

Haque

Abdelgawad

2020

View full text Add to dashboard Cite

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

show abstract

COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

Cited by 11 publications

References 78 publications

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System

A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

Contact Info

Product

Resources

About