Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures

Manickam, Adhiyaman; Jiang, Jianmin; Zhou, Yu; Sagar, Abhinav; Soundrapandiyan, Rajkumar; Samuel, R. Dinesh Jackson

doi:10.1016/j.measurement.2021.109953

Cited by 66 publications

(23 citation statements)

References 26 publications

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“…Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures [6] 93.06%…”

Section: Resnet50mentioning

confidence: 99%

“…An ensemble of three convolutional neural networks (GoogLeNet, ResNet18, and DenseNet-121) was proposed as a CAD system for pneumonia detection [5]. In [6], they identified the presence of pneumonia using U-Net architecture based segmentation and classified the pneumonia as normal and abnormal (bacteria, viral) using pretrained models such as ResNet50, InceptionV3, Inception-ResNetV2. Their results were analyzed and compared with other CNN models such as DenseNet-169 + SVM, VGG-16, RetinaNet + Mask RCNN, VGG-16 and Xception, Fully connected RCNN, etc using various measures.…”

Section: Introductionmentioning

confidence: 99%

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Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Palomo

Zafra-Santisteban

Luque‐Baena

2022

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAI

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Pneumonia is an infectious and deadly disease which strikes over millions of people. Usually, chest X-rays are used by radiotherapist to diagnose pneumonia. In this paper, a Computer-Aided Diagnosis (CAD) system for pneumonia detection in chest X-ray images is proposed. This system is based on Convolutional Neural Networks (CNNs) which are able to classify the image into two classes (pneumonia or normal). Experimental results show that the proposed system obtained an accuracy rate of 98.59%.

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“…Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures [6] 93.06%…”

Section: Resnet50mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Palomo

Zafra-Santisteban

Luque‐Baena

2022

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAI

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“…is importance reveals itself by discussing the successful diagnosis procedures as well as identifying the more accurate diagnosis tools for patients. Medical professionals face several challenges in their practice, and therefore healthcare research a ects the doctor or practitioner as much as it a ects the patient in terms of steering away from inaccurate diagnosis leading to unnecessary medication or other kind of treatment [1].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN

Alharbi

Alsaadi

Manimurugan

et al. 2022

Computational Intelligence and Neuroscience

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Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact precision. The typical detection method for COVID-19 infection is the RT-PCR but it is a rather expensive method which is also invasive and has a high margin of error. Radiographies are a good alternative for COVID-19 detection given the experience of the radiologist and his learning capabilities. To make an accurate detection from chest X-Rays, deep learning technologies can be involved to analyze the radiographs, learn distinctive patterns of coronavirus’ presence, find these patterns in the tested radiograph, and determine whether the sample is actually COVID-19 positive or negative. In this study, we propose a model based on deep learning technology using Convolutional Neural Networks and training it on a dataset containing a total of over 35,000 chest X-Ray images, nearly 16,000 for COVID-19 positive images, 15,000 for normal images, and 5,000 for pneumonia-positive images. The model’s performance was assessed in terms of accuracy, precision, recall, and F1-score, and it achieved 99% accuracy, 0.98 precision, 1.02 recall, and 99.0% F1-score, thus outperforming other deep learning models from other studies.

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“…Biomedical images, including computerized tomography (CT) and X-ray images, are popularly used to diagnose lung diseases like pneumonia [26][27][28][29], tuberculosis [30][31][32], interstitial lung diseases [33], early lung cancer [34][35][36][37], and pulmonary nodules [38][39][40][41][42][43]. The key advantages of using radiological images are the following: (a) they can be readily produced at any medical facility equipped with the necessary instrument and (b) the physicians need considerably less time to perform visual subjective diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques

Islam

Tarique

2022

International Journal of Biomedical Imaging

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This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.

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Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures

Cited by 66 publications

References 26 publications

Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN

Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques

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