Detection Of Covid-19 From Chest X-ray Images Using Deep Learning Techniques

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

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A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Sensors ◽

10.3390/s20123482 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3482

Author(s):

Abdullah-Al Nahid ◽

Niloy Sikder ◽

Anupam Kumar Bairagi ◽

Md. Abdur Razzaque ◽

Mehedi Masud ◽

...

Keyword(s):

Deep Learning ◽

Early Stage ◽

Machine Learning Techniques ◽

Past Century ◽

Radiographic Images ◽

X Ray ◽

The Past ◽

Learning Techniques ◽

Chest X Ray ◽

Pneumonia Diagnosis

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

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Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2020.102365 ◽

2021 ◽

Vol 64 ◽

pp. 102365

Author(s):

Soumya Ranjan Nayak ◽

Deepak Ranjan Nayak ◽

Utkarsh Sinha ◽

Vaibhav Arora ◽

Ram Bilas Pachori

Keyword(s):

Deep Learning ◽

X Ray ◽

Learning Techniques ◽

Chest X Ray ◽

Comprehensive Study

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Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

Mathematics ◽

10.3390/math8091423 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1423

Author(s):

Juan Eduardo Luján-García ◽

Marco Antonio Moreno-Ibarra ◽

Yenny Villuendas-Rey ◽

Cornelio Yáñez-Márquez

Keyword(s):

Neural Network ◽

Deep Learning ◽

Respiratory Diseases ◽

X Ray ◽

Aggressive Disease ◽

Learning Techniques ◽

The World ◽

Chest X Ray ◽

The Common ◽

Deep Learning Model

As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia.

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A holistic comparison between deep learning techniques to determine Covid-19 patients utilizing chest X-Ray images

Engineering and Applied Science Letters ◽

10.30538/psrp-easl2020.0054 ◽

2020 ◽

Vol 3 (4) ◽

pp. 85-93

Author(s):

Taki Hasan Rafi ◽

Keyword(s):

Deep Learning ◽

Medical Community ◽

Test Cases ◽

Suitable Model ◽

Testing Time ◽

Rt Pcr ◽

X Ray ◽

Learning Techniques ◽

Chest X Ray ◽

Novel Coronavirus

Novel coronavirus likewise called COVID-19 began in Wuhan, China in December 2019 and has now outspread over the world. Around 63 millions of people currently got influenced by novel coronavirus and it causes around 1,500,000 deaths. There are just about 600,000 individuals contaminated by COVID-19 in Bangladesh too. As it is an exceptionally new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for lower incoming countries to test cases easily. RT-PCR test is the most generally utilized analysis framework for COVID-19 patient detection. However, by utilizing X-ray image based programmed recognition can diminish the expense and testing time. So according to handling this test, it is important to program and effective recognition to forestall transmission to others. In this paper, author attempts to distinguish COVID-19 patients by chest X-ray images. Author executes various pre-trained deep learning models on the dataset such as Base-CNN, ResNet-50, DenseNet-121 and EfficientNet-B4. All the outcomes are compared to determine a suitable model for COVID-19 detection using chest X-ray images. Author also evaluates the results by AUC, where EfficientNet-B4 has 0.997 AUC, ResNet-50 has 0.967 AUC, DenseNet-121 has 0.874 AUC and the Base-CNN model has 0.762 AUC individually. The EfficientNet-B4 has achieved 98.86% accuracy.

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An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5528144 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Woan Ching Serena Low ◽

Joon Huang Chuah ◽

Clarence Augustine T. H. Tee ◽

Shazia Anis ◽

Muhammad Ali Shoaib ◽

...

Keyword(s):

Deep Learning ◽

Viral Infection ◽

Technical Expertise ◽

Human Errors ◽

X Ray ◽

Learning Techniques ◽

Technology Improvement ◽

Life Threatening ◽

Chest X Ray ◽

Made In

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

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Pneumonia Classification using Deep Learning in Healthcare

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1599.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1715-1723 ◽

Cited By ~ 3

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Research Work ◽

Neural System ◽

X Ray ◽

Python Language ◽

Learning Techniques ◽

Improved Outcomes ◽

Augmentation Techniques ◽

Chest X Ray

There is a great growing interest in the domain of deep learning techniques for identifying and classifying images with various datasets. An enormous availability of datasets (e.g. ChestX-Ray14 dataset) has developed a keen interest in deep learning. Pneumonia is a disease that is caused by various bacteria, virus etc. X-ray is one of the major diagnosis tools for diagnosing pneumonia. This research work mainly proposes a convolutional neural system (CNN) model prepared without any preparation to group and identify the occurrence of pneumonia disease from a given assortment of chest X-ray image tests. Dissimilar to different strategies that depend exclusively on more learning draws near or conventional carefully assembled systems to accomplish an amazing grouping execution, and developed a convolutional neural arrange model without any preparation to separate and character the images to decide whether an individual is suffering with pneumonia. This model could help alleviate the dependability and difficult challenges frequently confronted to manage therapeutic problems. In this paper, CNN algorithm has been used along with different data augmentation techniques for improving the classification accuracies which has been discussed to increase the performance which will help in improving the validation and training accuracies and characterization of exactness of the CNN model and accomplished various results. This experiment was carried out using python language and has shown improved outcomes.

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