A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

Chouhan, V. S.; Singh, Sanjay Kumar; Khamparia, Aditya; Gupta, Deepak; Tiwari, Prayag; Moreira, Catarina; Damaševičius, Robertas; Albuquerque, Victor Hugo C. de

doi:10.3390/app10020559

Cited by 544 publications

(356 citation statements)

References 55 publications

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“…Chouhan et al [48] introduced an ensemble deep model that combines outputs from all transfer deep models for the classification of pneumonia using the connotation of deep learning. The Guangzhou Medical Center [44] database introduced a total of approximately 5200 X-ray images, divided to 1300 X-ray normal, 3900 X-rays abnormal.…”

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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“…This idea has been successfully applied to visual recognition [21] as well as language comprehension [22]. In the medical domain, transfer learning has also been widely used in medical image classification and recognition tasks, such as tumor classification [23], retinal diseases diagnosis [24], pneumonia detection [25], and skin lesion and cancer classification [26], [27]. A recent study in [28] explores the properties of transfer learning for medical imaging tasks and finds that the standard large networks pretrained on ImageNet are often over-parameterized and may not be the optimal solution for medical image diagnosis.…”

Section: Transfer Learningmentioning

confidence: 99%

Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans

He¹,

Yang²,

Zhang

et al. 2020

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Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based on CT scans. However, these works are difficult to reproduce and adopt since the CT data used in their studies are not publicly available. Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain. In this paper, we aim to address these two problems. We build a publicly-available dataset containing hundreds of CT scans positive for COVID-19 and develop sample-efficient deep learning methods that can achieve high diagnosis accuracy of COVID-19 from CT scans even when the number of training CT images are limited. Specifically, we propose an Self-Trans approach, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting. Extensive experiments demonstrate the superior performance of our proposed Self-Trans approach compared with several state-of-the-art baselines. Our approach achieves an F1 of 0.85 and an AUC of 0.94 in diagnosing COVID-19 from CT scans, even though the number of training CTs is just a few hundred.

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“…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. We will refer to Lian and Zheng network as LZNet2019 and to Chouhan et al ensemble as Cho2020 for further comparisons.…”

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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A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

Cited by 544 publications

References 55 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

Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans

A Transfer Learning Method for Pneumonia Classification and Visualization

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