scholarly journals A Comparative Study of Multiple Neural Network for Detection of Covid 19 on Chest X-Ray

2021 ◽  
Author(s):  
Anis Shazia ◽  
Tan Zi Xuan ◽  
Joon Huang Chuah ◽  
Juliana Usman ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Comparative Study ◽  
Healthcare Workers ◽  
Performance Metrics ◽  
Medical Image Analysis ◽  
X Ray ◽  
Research Areas ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Performance ◽  
Multiple Neural Network

Abstract Coronavirus disease of 2019 or Covid-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers it is becoming over-whelming for the healthcare workers ta rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with detection and classification of coronavirus pneumonia from other pneumonia cases. This study uses 7,165 chest X-ray images of Covid-19 (1536) and Pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

scholarly journals A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Anis Shazia ◽  
Tan Zi Xuan ◽  
Joon Huang Chuah ◽  
Juliana Usman ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Comparative Study ◽  
Healthcare Workers ◽  
Performance Metrics ◽  
Medical Image Analysis ◽  
X Ray ◽  
Research Areas ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Performance ◽  
Multiple Neural Network

AbstractCoronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

A Comparative Study Between Planar Ga-67, TI-201 Images, Chest X-Ray, and X-Ray CT in Inoperable Non-Small Cell Carcinoma of the Lung

1995 ◽  
Vol 20 (5) ◽  
pp. 426-433 ◽  
Author(s):  
AHMAD M. RAGHEB ◽  
ABDEL-HAMEED H. ELGAZZAR ◽  
ALY K. IBRAHIM ◽  
EZZATT HIGAZI ◽  
ABDEL-RAHMAN MAHMOUD ◽  
...  
Keyword(s):  
Comparative Study ◽  
Cell Carcinoma ◽  
Small Cell Carcinoma ◽  
Small Cell ◽  
X Ray ◽  
Chest X Ray

scholarly journals Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

2021 ◽  
Author(s):  
Hamzeh Asgharnezhad ◽  
Afshar Shamsi ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Objective Evaluation ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Uncertainty Estimates ◽  
Chest X Ray ◽  
Novel Concept

Abstract Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate where and when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

scholarly journals Comparison of Different Diagnostic Method for Mycobacteria Tuberculosis in Suspected Patients

1970 ◽  
Vol 7 (2) ◽  
pp. 84-88
Author(s):  
AR Khagi ◽  
S Singh ◽  
S Subba ◽  
A Bajracharya ◽  
R Tuladhar ◽  
...  
Keyword(s):  
Comparative Study ◽  
Sputum Smear ◽  
Study Group ◽  
Bacterial Cells ◽  
X Ray ◽  
Smear Examination ◽  
Group A ◽  
Chest X Ray ◽  
Group B ◽  
Sputum Smear Examination

Background: Microbial examination of smear of AFB by Z-N stain is currently the most rapid method for the detection of M. tuberculosis but its sensitivity is low i.e. required at least 10,000 bacterial cells per ml of sputum and also none specific, but auramine staining method has higher sensitivity than that of the Z-N stain but there are chances of false positive. Objective of this study was to find the correlation between chest X-ray, direct sputum smear examination by Ziehl-Neelsen stain, Auramine fluorochrome stain and sputum culture for M. tuberculosis. Methods: During that study period 250 x 3 samples were taken three each from 250 patients and divided into two groups A and B by performing Auramine fluorochrome stain in all samples . In group A, there were 150 fluorochrome stain positive samples. One each from 150 patient for comparative study of direct sputum smear examination by Ziehl-Neelsen stain, , culture on LJ medium and chest X-ray. Similarly in group B, next 100 fluorochrome stain negative specimens one each from 100 patients were taken for the comparative study of direct sputum smear examination by Ziehl-Neelsen stain, culture and chest X-ray. Results: In the study group A (n=150) all the specimens were positive in Auramine fluorochrome stain and all of them show positive in X-ray but only 134 showed positive in Ziehl-Neelsen stain and 136 showed positive in culture. In the study group B (n=100), all the specimens were negative in Auramine fluorochrome stain and all of them show negative in Ziehl-Neelsen stain but 14 of them were positive in culture and 24 were positive in chest X-ray. Conclusions: The diagnosis of PTB could be made by Auramine fluorochrome microscopy and culture. Key words: auramine fluorochrome stain; culture; mycobacterium tuberculosis; x-ray; ziehl-neelsen. DOI: 10.3126/jnhrc.v7i2.3012 Journal of Nepal Health Research Council Vol.7(2) Apr 2009 84-88

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 Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

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