Pneumonia Detection Using CNN based Feature Extraction

Varshni, Dimpy; Thakral, Kartik; Agarwal, Lucky; Nijhawan, Rahul; Mittal, Ankush

doi:10.1109/icecct.2019.8869364

Cited by 307 publications

(124 citation statements)

References 18 publications

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“…Li et al (2018) used Den-seNet-121 and DenseNet-RNN were two deep learning models utilized to analyze the infections in ChestX-Ray14, where DenseNet-121 getting a sum of 74.5% and DenseNet-RNN was 75.1% to recognizing Pneumonia. Rajpurkar et al (2017) were introduced by taking 121 layers to identify one of the 14 infections at 76.8% of accuracy of the pneumonic class from the others; likewise this model gives a heatmap for possible localization that depends on the forecast done by the convolutional neural network, and more study can be found by applying machine learning and deep learning algorithm to analyze the X-ray and CT images (Basu et al 2020;Pavithra et al 2015;Ozkaya et al 2020;Santos and Melin 2020;Tolga et al 2020;Ramírez et al 2019;Miramontes et al 2018;Melin et al 2018;Kermany, et al 2018b, a;Ayan, and Ü nver, 2019;Varshni et al 2019;Wang et al 2017;Togaçar et al 2019;Jaiswal, et al 2019;Sirazitdinov, et al 2019;Behzadi-khormouji et al 2020;Stephen et al 2019;Xu et al 2020;Shan et al 2020).…”

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confidence: 99%

RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models. Keywords COVID-19 Á X-ray images Á CT scan Á CNN Á VGG-16 Á Inception_V2 Á Decision tree 1 Introduction The first case of the virus became exposed in Wuhan city of China in November 2019, there are 1,100,000 peoples living in this city and it interfaces numerous urban communities of China. The outbreak of atypical and individual-to-individual transmissible pneumonia brought about by the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) has caused a worldwide. There have been in excess of 26,000,000 confirmed cases of the corona virus disease (COVID-19) on the globe, as of April 23, 2020. As indicated by the WHO, 16-21% of individuals with the infection have gotten seriously sick with a Communicated by Valentina E. Balas.

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RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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“…X-ception network is more felicitous for classifying X-ray images than VGG-16 network. Varshni et al [46] proposed pre-trained ConvNet models (VGG-16, Xception, Res50, Dense-121, and Dense-169) as feature-extractors followed by different classifiers Symmetry 2020, 12, 651 5 of 19 (SVM, Random Forest, k-nearest neighbors, Naïve Bayes) for the detection of normal and abnormal pneumonia X-rays images. The prosaists used ChestX-ray14 introduced by Wang et al [47].…”

Section: Related Workmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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“…The usage of deep learning models in medical image processing and analysis is a challenging topic in the AI field [10,20]. In [21], the authors propose a CNN model for pneumonia detection. The authors of the study [22] propose a vessel extraction from the fundus images.…”

Section: Introductionmentioning

confidence: 99%

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

2020

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The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.

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Pneumonia Detection Using CNN based Feature Extraction

Cited by 307 publications

References 18 publications

RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

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