A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system

Chagas, João Victor Souza das; Rodrigues, Douglas de A.; Ivo, Roberto F.; Hassan, Mohammad Mehedi; Albuquerque, Victor Hugo C. de

doi:10.1007/s11554-021-01086-y

Cited by 22 publications

(6 citation statements)

References 31 publications

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“…Pneumonia, asthma, lung cancer, and Tuberculosis are some of the most common respiratory illnesses. A person's symptoms and the findings of computed tomography (CT) scans and chest x-rays (CXR) images are used to diagnose HAV nowadays [11,12]. Several regulatory bodies have authorized breath testing to identify lung infections in people.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

Baskar

2022

Preprint

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Ventilator-associated pneumonia is the leading cause of death in intensive care units (ICU). Hospital-acquired pneumonia is twice as common in non-ventilated patients as it is in ventilated patients, according to a recent study. Exhaled breath samples can be used to distinguish between ill and healthy individuals by identifying volatile organic compounds (VOCs) in the breath. A reliable and non-invasive method of diagnosing VOC-induced pneumonia is essential for prompt treatment and decreasing fatality rates. Researchers have used wearable biosensors and machine learning (ML) algorithms to diagnose pneumonia to analyze VOCs in exhaled air. Wireless body area networks (WBANs) offer the potential for wearable sensors and internet-enabled ICU monitoring when it comes to health monitoring. These results imply that a wearable biosensor-based IoT-based machine learning system may identify ventilator- and hospital-acquired pneumonia from exhaled VOC components. The developed device was deployed on an NVIDIA Jetson Nano graphical processing unit, allowing for the seamless and immediate transmission of volatile organic compound (VOC) data and other patient biological attributes such as temperature and blood oxygen saturation (SpO2) to the Amazon Web Services IoT Core. VOC and ML-aided techniques have proven that artificial intelligence can reliably discriminate pneumonia samples from control samples, and the SVM model outperforms the K Nearest Neighbors model in terms of accuracy (91.38%), sensitivity (92.7%), precision (92.8%), and receiver operating characteristic (ROC) (91.01 percent).

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

Baskar

2022

Preprint

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“…The result shows the second mechanism provides the highest accuracy yet this method classifies a single disease. In 2021 das Chagas et al [25] established an IoT-based novel technique to detect pneumonia in children using X-ray scans. In this model, 12 pre-trained CNN networks are chosen as feature extractors, and the approaches were integrated with classification learning models.…”

Section: Related Workmentioning

confidence: 99%

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

Prabakaran¹,

Selvaraj²

2023

Computer Systems Science and Engineering

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In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

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“…Using CT scan images and transfer learning techniques, Souza et al utilized Mask R-CNN for lung segmentation in 2021 [150]. In the medical industry, CT scan and CXR images are employed for a variety of purposes [9,10]. Deep Learning [11], Machine Learning [12], and Image Processing [13] are some of the AI technologies that can be utilised to distinguish COVID-19 patients from CAP (Community Acquired Patients) [14].…”

Section: Introductionmentioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

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COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with.

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A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system

Cited by 22 publications

References 31 publications

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

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