555 research outputs found
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Pneumonia is a life-threatening disease, which occurs in the lungs caused by
either bacterial or viral infection. It can be life-endangering if not acted
upon in the right time and thus an early diagnosis of pneumonia is vital. The
aim of this paper is to automatically detect bacterial and viral pneumonia
using digital x-ray images. It provides a detailed report on advances made in
making accurate detection of pneumonia and then presents the methodology
adopted by the authors. Four different pre-trained deep Convolutional Neural
Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for
transfer learning. 5247 Bacterial, viral and normal chest x-rays images
underwent preprocessing techniques and the modified images were trained for the
transfer learning based classification task. In this work, the authors have
reported three schemes of classifications: normal vs pneumonia, bacterial vs
viral pneumonia and normal, bacterial and viral pneumonia. The classification
accuracy of normal and pneumonia images, bacterial and viral pneumonia images,
and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3%
respectively. This is the highest accuracy in any scheme than the accuracies
reported in the literature. Therefore, the proposed study can be useful in
faster-diagnosing pneumonia by the radiologist and can help in the fast airport
screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with
arXiv:2003.1314
Two-stage deep learning architecture for pneumonia detection and its diagnosis in chest radiographs
Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such as AlexNet, ResNet, VGG16 and Inception-v3 for detection of pneumonia. For these networks, we employ transfer learning to exploit the wealth of information available from prior training. For the second stage, we find that transfer learning with these same networks tends to overfit the data. For this reason we propose a simpler CNN architecture for classification of pneumonic chest radiographs and show that it overcomes the overfitting problem. We further enhance the performance of our system in a novel way by incorporating lung segmentation using a U-Net architecture. We make use of a publicly available dataset comprising 5856 images (1583 - Normal, 4273 - Pneumonic). Among the pneumonia patients, 2780 patients are identified as bacteria type and the rest belongs to virus category. We test our proposed algorithm(s) on a set of 624 images and we achieve an area under the receiver operating characteristic curve of 0.996 for pneumonia detection. We also achieve an accuracy of 97.8% for classification of pneumonic chest radiographs thereby setting a new benchmark for both detection and diagnosis. We believe the proposed two-stage classification of chest radiographs for pneumonia detection and its diagnosis would enhance the workflow of radiologists
A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images
Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time
Can AI help in screening Viral and COVID-19 pneumonia?
Coronavirus disease (COVID-19) is a pandemic disease, which has already
caused thousands of causalities and infected several millions of people
worldwide. Any technological tool enabling rapid screening of the COVID-19
infection with high accuracy can be crucially helpful to healthcare
professionals. The main clinical tool currently in use for the diagnosis of
COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which
is expensive, less-sensitive and requires specialized medical personnel. X-ray
imaging is an easily accessible tool that can be an excellent alternative in
the COVID-19 diagnosis. This research was taken to investigate the utility of
artificial intelligence (AI) in the rapid and accurate detection of COVID-19
from chest X-ray images. The aim of this paper is to propose a robust technique
for automatic detection of COVID-19 pneumonia from digital chest X-ray images
applying pre-trained deep-learning algorithms while maximizing the detection
accuracy. A public database was created by the authors combining several public
databases and also by collecting images from recently published articles. The
database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579
normal chest X-ray images. Transfer learning technique was used with the help
of image augmentation to train and validate several pre-trained deep
Convolutional Neural Networks (CNNs). The networks were trained to classify two
different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and
COVID-19 pneumonia with and without image augmentation. The classification
accuracy, precision, sensitivity, and specificity for both the schemes were
99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%,
respectively.Comment: 12 pages, 9 Figure
Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance
People all over the globe are affected by pneumonia but deaths due to it are
highest in Sub-Saharan Asia and South Asia. In recent years, the overall
incidence and mortality rate of pneumonia regardless of the utilization of
effective vaccines and compelling antibiotics has escalated. Thus, pneumonia
remains a disease that needs spry prevention and treatment. The widespread
prevalence of pneumonia has caused the research community to come up with a
framework that helps detect, diagnose and analyze diseases accurately and
promptly. One of the major hurdles faced by the Artificial Intelligence (AI)
research community is the lack of publicly available datasets for chest
diseases, including pneumonia . Secondly, few of the available datasets are
highly imbalanced (normal examples are over sampled, while samples with ailment
are in severe minority) making the problem even more challenging. In this
article we present a novel framework for the detection of pneumonia. The
novelty of the proposed methodology lies in the tackling of class imbalance
problem. The Generative Adversarial Network (GAN), specifically a combination
of Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein
GAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia''
for augmentation, whereas Random Under-Sampling (RUS) was done on the majority
class ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8
dataset, one of the biggest datasets, is used to validate the performance of
the proposed framework. The learning phase is completed using transfer learning
on state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16.
Results obtained exceed state-of-the-art
Evaluation of tuberculosis diagnostic test accuracy using Bayesian latent class analysis in the presence of conditional dependence between the diagnostic tests used in a community-based tuberculosis screening study
Diagnostic accuracy studies in pulmonary tuberculosis (PTB) are complicated by the lack of a perfect reference standard. This limitation can be handled using latent class analysis (LCA), assuming independence between diagnostic test results conditional on the true unobserved PTB status. Test results could remain dependent, however, e.g. with diagnostic tests based on a similar biological basis. If ignored, this gives misleading inferences. Our secondary analysis of data collected during the first year (May 2018 -May 2019) of a community-based multi-morbidity screening program conducted in the rural uMkhanyakude district of KwaZulu Natal, South Africa, used Bayesian LCA. Residents of the catchment area, aged >/=15 years and eligible for microbiological testing, were analyzed. Probit regression methods for dependent binary data sequentially regressed each binary test outcome on other observed test results, measured covariates and the true unobserved PTB status. Unknown model parameters were assigned Gaussian priors to evaluate overall PTB prevalence and diagnostic accuracy of 6 tests used to screen for PTB: any TB symptom, radiologist conclusion, Computer Aided Detection for TB version 5 (CAD4TBv5>/=53), CAD4TBv6>/=53, Xpert Ultra (excluding trace) and culture. Before the application of our proposed model, we evaluated its performance using a previously published childhood pulmonary TB (CPTB) dataset. Standard LCA assuming conditional independence yielded an unrealistic prevalence estimate of 18.6% which was not resolved by accounting for conditional dependence among the true PTB cases only. Allowing, also, for conditional dependence among the true non-PTB cases produced a 1.1% plausible prevalence. After incorporating age, sex, and HIV status in the analysis, we obtained 0.9% (95% CrI: 0.6, 1.3) overall prevalence. Males had higher PTB prevalence compared to females (1.2% vs. 0.8%). Similarly, HIV+ had a higher PTB prevalence compared to HIV- (1.3% vs. 0.8%). The overall sensitivity for Xpert Ultra (excluding trace) and culture were 62.2% (95% CrI: 48.7, 74.4) and 75.9% (95% CrI: 61.9, 89.2), respectively. Any chest X-ray abnormality, CAD4TBv5>/=53 and CAD4TBv6>/=53 had similar overall sensitivity. Up to 73.3% (95% CrI: 61.4, 83.4) of all true PTB cases did not report TB symptoms. Our flexible modelling approach yields plausible, easy-to-interpret estimates of sensitivity, specificity and PTB prevalence under more realistic assumptions. Failure to fully account for diagnostic test dependence can yield misleading inferences
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