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

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rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated.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.

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

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1742
Author(s):  
Edoardo Vantaggiato ◽  
Emanuela Paladini ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
High Performance ◽  
Recognition Accuracy ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Architectures ◽  
Made In

The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts that have been made in this field since the appearance of COVID-19 (2019), the field still suffers from two drawbacks. First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. Second, all the works that have been carried out in the field are separate; there are no unified data, classes, and evaluation protocols. In this work, based on public and newly collected data, we propose two X-ray COVID-19 databases, which are three-class COVID-19 and five-class COVID-19 datasets. For both databases, we evaluate different deep learning architectures. Moreover, we propose an Ensemble-CNNs approach which outperforms the deep learning architectures and shows promising results in both databases. In other words, our proposed Ensemble-CNNs achieved a high performance in the recognition of COVID-19 infection, resulting in accuracies of 100% and 98.1% in the three-class and five-class scenarios, respectively. In addition, our approach achieved promising results in the overall recognition accuracy of 75.23% and 81.0% for the three-class and five-class scenarios, respectively. We make our databases of COVID-19 X-ray scans publicly available to encourage other researchers to use it as a benchmark for their studies and comparisons.

scholarly journals Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

2022 ◽  
Vol 70 (1) ◽  
pp. 1541-1556
Author(s):  
Shubham Mahajan ◽  
Akshay Raina ◽  
Mohamed Abouhawwash ◽  
Xiao-Zhi Gao ◽  
Amit Kant Pandit
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

2020 ◽  
Vol 77 (9) ◽  
pp. 597-602
Author(s):  
Xiaohua Wang ◽  
Juezhao Yu ◽  
Qiao Zhu ◽  
Shuqiang Li ◽  
Zanmei Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
X Ray ◽  
Testing Dataset ◽  
Learning Techniques ◽  
History Of ◽  
Digital Chest ◽  
Chest X Ray

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.

scholarly journals A Deep Learning Method for Alerting Emergency Physicians about the Presence of Subphrenic Free Air on Chest Radiographs

2021 ◽  
Vol 10 (2) ◽  
pp. 254
Author(s):  
Che-Yu Su ◽  
Tsung-Yu Tsai ◽  
Cheng-Yen Tseng ◽  
Keng-Hao Liu ◽  
Chi-Wei Lee
Keyword(s):  
Deep Learning ◽  
Emergency Physicians ◽  
Timely Manner ◽  
X Ray ◽  
Automated Method ◽  
Free Air ◽  
Life Threatening ◽  
Hollow Organ ◽  
Chest X Ray ◽  
Sensitivity Specificity

Hollow organ perforation can precipitate a life-threatening emergency due to peritonitis followed by fulminant sepsis and fatal circulatory collapse. Pneumoperitoneum is typically detected as subphrenic free air on frontal chest X-ray images; however, treatment is reliant on accurate interpretation of radiographs in a timely manner. Unfortunately, it is not uncommon to have misdiagnoses made by emergency physicians who have insufficient experience or who are too busy and overloaded by multitasking. It is essential to develop an automated method for reviewing frontal chest X-ray images to alert emergency physicians in a timely manner about the life-threatening condition of hollow organ perforation that mandates an immediate second look. In this study, a deep learning-based approach making use of convolutional neural networks for the detection of subphrenic free air is proposed. A total of 667 chest X-ray images were collected at a local hospital, where 587 images (positive/negative: 267/400) were used for training and 80 images (40/40) for testing. This method achieved 0.875, 0.825, and 0.889 in sensitivity, specificity, and AUC score, respectively. It may provide a sensitive adjunctive screening tool to detect pneumoperitoneum on images read by emergency physicians who have insufficient clinical experience or who are too busy and overloaded by multitasking.

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

Sensors ◽  
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.

scholarly journals Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Surya Krishnamurthy ◽  
Kathiravan Srinivasan ◽  
Saeed Mian Qaisar ◽  
P. M. Durai Raj Vincent ◽  
Chuan-Yu Chang
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Large Scale ◽  
Data Augmentation ◽  
Medical Image Analysis ◽  
Predictive Performance ◽  
X Ray ◽  
Recent Developments ◽  
Life Threatening ◽  
Chest X Ray

Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.

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

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

scholarly journals Screening of chest X-Rays for Tuberculosis using Deep Convolutional Neural Network

2021 ◽  
Vol 9 (5) ◽  
pp. 254-258
Author(s):  
R. Rohith ◽  
S.P.Syed Ibrahim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Medical Officer ◽  
Developing Nations ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray

Tuberculosis is a life-threatening disease that mainly affects underdeveloped as well as developing nations. While lethal it is often resistive to antibiotics and the safest way to treat a patient is to detect the disease's presence as soon as possible. Various techniques have been developed to diagnose tuberculosis and radiography of the chest is one of such methods that works well for over a decade.. Though an effective method still the success depends on the medical officer who examines the chest X-rays. Thus ,this paper proposes an approach for detecting X-ray abnormalities using deep learning. The systems output is assessed on two open Montgomery and Shenz en chest X-ray datasets and accuracy of 84 percent is achieved.

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