An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

Stephen, Okeke; Sain, Mangal; Maduh, Uchenna Joseph; Jeong, Do-Un

doi:10.1155/2019/4180949

Cited by 482 publications

(288 citation statements)

References 16 publications

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“…Classical image classification stages can be divided into three main stages: image preprocessing, feature extraction, and feature classification. Stephen et al [43] proposed a new study of classifying and detect the presence of pneumonia from a collection of chest X-ray image samples based on a ConvNet model trained from scratch based on dataset [44]. The outcomes obtained were training loss = 12.88%, training accuracy = 95.31%, validation loss = 18.35%, and validation accuracy = 93.73%.…”

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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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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“…Therefore, due to the popularity of CX14 dataset, we can find several works that use CX14 dataset and present novel results almost every two or three months, examples of these works are found in [32][33][34][35].…”

Section: Transfer Learning and Chest Diseasesmentioning

confidence: 99%

“…For Kermany's dataset, there exist several classification works, like the one presented by Stephen et al [34]. One of the most recent top results was by Liang and Zheng using an individual CNN model [37] and the most recent work by Chouhan et al [38], that used an ensemble model.…”

Section: Transfer Learning and Chest Diseasesmentioning

confidence: 99%

A Transfer Learning Method for Pneumonia Classification and Visualization

et al. 2020

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Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

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“…However, physicians are still a long way from understanding how machine learning, neural networks or, most importantly, artificial intelligence (AI) tools can further current medical practice. Six studies have recently been published [4][5][6][7][8][9] using AI approaches to support pneumonia diagnosis and empirical antibiotic decision-making processes. Most of the research has been conducted in the field of pneumonia diagnosis through the study of chest radiographs.…”

Section: @Erspublicationsmentioning

confidence: 99%

“…In contrast to the single image available to clinicians when analysing a chest radiograph, image processing performed by AI tools can break down an image's architecture into millions of pixels and provide far more accurate computerised discrimination. Therefore, the computer's ability to perform optimal pneumonia diagnosis based on chest radiograph analysis is much greater than that of a human [5].…”

Section: @Erspublicationsmentioning

confidence: 99%

Macrolide combination therapy for hospitalised CAP patients? An individualised approach supported by machine learning

2019

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@ERSpublicationsThe article by König and co-workers paves the way for the selection of a subset of patients with CAP in whom combination initial therapy including macrolides could improve outcomes using a new and interesting mathematical approach http://bit.ly/2p8RdCDCite this article as: Garcia-Vidal C, Cillóniz C, Torres A. Macrolide combination therapy for hospitalised CAP patients? An individualised approach supported by machine learning. Eur Respir J 2019; 54: 1902111 [https://doi.org/10.1183/13993003.02111-2019.The use of macrolides in combination with betalactams to improve outcomes in patients with community-acquired pneumonia (CAP) remains a topic of controversy, mainly because most of the evidence comes from observational studies rather than from randomised clinical trials (RCTs). Some recent studies have suggested that macrolides are effective in patients with Streptococcus pneumoniae infection plus a high systemic inflammatory response [1]. However, macrolides can be harmful [2] and there is a clear need to identify CAP phenotypes that would benefit from macrolides without suffering negative effects. We are unlikely to solve this question using conventional study designs; rather, we need to explore new technologies, among them the use of models obtained with machine learning methodology.In an interesting paper, KÖNIG et al.[3] used new statistical tools, combining propensity matching score with tree decision models (not specified) using non-supervised machine learning (also not specified) to identify patients in whom macrolide use would be beneficial. They found that a simple decision tree of pneumonia patients' characteristics, comprising chronic cardiovascular and chronic respiratory co-morbidities and leukocyte counts in the respiratory secretion at admission, was able to identify the ones who would achieve better outcomes with the addition of macrolide treatment. The results are important and open up new perspectives for conducting optimised, randomised trials to demonstrate the beneficial role of macrolides in patients with CAP.But probably one of the most interesting features of this paper is the incorporation of new statistical tools in medical research. The machine learning approach involves the study of algorithms used by computer systems to perform a specific task, most of them associated with predictions, without using explicit instructions that rely on patterns. Machine learning builds a mathematical model based on training data in order to make predictions in a test dataset. It includes a heterogeneous range of approaches, such as

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An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

Cited by 482 publications

References 16 publications

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

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

A Transfer Learning Method for Pneumonia Classification and Visualization

Macrolide combination therapy for hospitalised CAP patients? An individualised approach supported by machine learning

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