Detection of Pneumonia from X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Author(s):  
Saranya P ◽  
TanmayThapliyal ◽  
AvibhashBajpai
Keyword(s):  
Network Architecture ◽  
Accurate Analysis ◽  
X Ray ◽  
Naked Eye ◽  
Network Layers ◽  
Chest X Ray ◽  
Lateral Chest ◽  
Usual Process ◽  
Aided Diagnosis ◽  
Precision Rate

Pneumonia is an infection which is caused by bacteria or viruses. Early diagnosis is critical to treat the disease successfully without delaying the treatment much. In most of the cases and as per the usual process the patient with pneumonia-like symptoms can be dragonized via frontal and lateral chest x-ray images, which are then seen over by the naked eye by doctors or radiologists. The diagnoses can be misleading and confusing as the appearance of the disease can be unclear in X-ray images and can put the doctor in a dilemma, as the features may not be visible clearly via naked eyes. That is why computer-aided diagnosis is generally required to guide clinicians. The model is based upon the convolutional neural network architecture, wherein pre-processed images are fed to the developed network layers and trained to provide us results with high accuracy of 94.3%, a precision rate of 93.18%, recall of 98.20% and an F1 score of 95.63%. The objective of the work is to design a model that can provide fast and accurate analysis which not only may save diagnosis cost, but also provide invaluable time for the doctors to begin the treatment if the disease is detected early.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

Double Cardiac Silhouette on Lateral Chest X-Ray Graphy

2011 ◽  
Vol 29 (2) ◽  
pp. E52-E53 ◽  
Author(s):  
Ozcan Basaran ◽  
Ahmet Guler ◽  
Can Y. Karabay ◽  
Soe M. Aung ◽  
Arzu Kalayci ◽  
...  
Keyword(s):  
X Ray ◽  
Chest X Ray ◽  
Lateral Chest ◽  
Cardiac Silhouette

Diagnosis of vertebral fractures on lateral chest X-ray: Intraobserver agreement of semi-quantitative vertebral fracture assessment

2014 ◽  
Vol 83 (12) ◽  
pp. 2177-2180 ◽  
Author(s):  
H.C. van der Jagt-Willems ◽  
B.C. van Munster ◽  
M. Leeflang ◽  
E. Beuerle ◽  
C.R. Tulner ◽  
...  
Keyword(s):  
Vertebral Fracture ◽  
Vertebral Fractures ◽  
Vertebral Fracture Assessment ◽  
Intraobserver Agreement ◽  
X Ray ◽  
Chest X Ray ◽  
Fracture Assessment ◽  
Lateral Chest

scholarly journals Aggravation and deliberation over lung herniation

2019 ◽  
Vol 12 (5) ◽  
pp. e229225
Author(s):  
Michelle N Lee ◽  
Luke T Surry ◽  
David M Ferraro
Keyword(s):  
Influenza A ◽  
Surgical Repair ◽  
Caucasian Woman ◽  
Pleuritic Chest Pain ◽  
X Ray ◽  
Lung Herniation ◽  
Left Posterior ◽  
History Of ◽  
Chest X Ray ◽  
Lateral Chest

A Caucasian woman aged 58 years with history of asthma and surgically repaired congenital diaphragmatic hernia presented to the emergency department (ED) with persistent cough, pleuritic chest pain, shortness of breath, in spite of recent treatment for influenza A virus. On physical examination, a large bulge was protruding from her left posterior thorax. She was found to have a large abnormal radiographic lucency on lateral chest X-ray posterior to the thoracic cavity, confirmed with chest CT to represent a large lung herniation in between the left seventh and eighth ribs. The patient was evaluated by a thoracic surgeon and offered surgical repair but ultimately decided on conservative management which to date has been ineffective.

scholarly journals COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

2020 ◽  
Vol 10 (16) ◽  
pp. 5683 ◽  
Author(s):  
Lourdes Duran-Lopez ◽  
Juan Pedro Dominguez-Morales ◽  
Jesús Corral-Jaime ◽  
Saturnino Vicente-Diaz ◽  
Alejandro Linares-Barranco
Keyword(s):  
Deep Learning ◽  
Imaging Techniques ◽  
Learning System ◽  
Screening Process ◽  
X Ray ◽  
Average Accuracy ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Control ◽  
Aided Diagnosis

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

scholarly journals Public Covid-19 X-ray datasets and their impact on model bias - a systematic review of a significant problem

2021 ◽  
Author(s):  
Beatriz Garcia Santa Cruz ◽  
Matías Nicolás Bossa ◽  
Jan Sölter ◽  
Andreas Dominik Husch
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Systematic Evaluation ◽  
Bias Assessment ◽  
X Ray ◽  
Inappropriate Use ◽  
Chest X Ray ◽  
Almost All ◽  
Aided Diagnosis

ABSTRACTComputer-aided-diagnosis for COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. This study provides a systematic evaluation of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias.Only 5 out of 256 identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably almost all of the datasets utilised in 78 papers published in peer-reviewed journals, are not among these 5 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use.This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

scholarly journals Precise Prediction of COVID-19 in Chest X-Ray Images Using KE Sieve Algorithm

2020 ◽  
Author(s):  
S Sai Thejeshwar ◽  
Chaitanya Chokkareddy ◽  
K Eswaran
Keyword(s):  
Ct Scan ◽  
Network Architecture ◽  
Intelligent Systems ◽  
Imaging Techniques ◽  
Health Care Systems ◽  
Prediction Method ◽  
Lung Infection ◽  
Disease Diagnosis ◽  
X Ray ◽  
Chest X Ray

The novel coronavirus (COVID-19) pandemic is pressurizing the healthcare systems across the globe and few of them are on the verge of failing. The detection of this virus as early as possible will help in contaminating the spread of it as the virus is mutating itself as fast as possible and currently there are about 4,300 strains of the virus according to the reports. Clinical studies have shown that most of the COVID-19 patients suffer from a lung infection similar to influenza. So, it is possible to diagnose lung infection using imaging techniques. Although a chest computed tomography (CT) scan has been shown to be an effective imaging technique for lung-related disease diagnosis, chest X-ray is more widely available across the hospitals due to its considerably lower cost and faster imaging time than CT scan. The advancements in the area of machine learning and pattern recognition has resulted in intelligent systems that analyze CT Scans or X-ray images and classify between pneumonia and normal patients. This paper proposes KE Sieve Neural Network architecture, which helps in the rapid diagnosis of COVID-19 using chest X-ray images. This architecture is achieving an accuracy of 98.49%. This noninvasive prediction method can assist the doctors in this pandemic and reduce the stress on health care systems.

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