Chest CT Findings and Differential Diagnosis of Mycoplasma pneumoniae Pneumonia and Mycoplasma pneumoniae Combined with Streptococcal Pneumonia in Children

Wang, Jing; Chen, Xia; Sharma, Ashutosh; Gaba, Gurjot Singh; Shabaz, Mohammad

doi:10.1155/2021/8085530

Cited by 28 publications

(25 citation statements)

References 43 publications

(42 reference statements)

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“…These images are directly visible on a computer monitor in a realistic manner. This enables the doctor to analyze the size, shape, rigidity, and place of doubtful deposits at the same time [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people’s inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k -means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.

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Section: Introductionmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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“…This paper presents comparative analysis over supervised machine learning (SVM) and graph technique to identify abnormal patient profiles for reliable healthcare data over two healthcare dataset, i.e., Pubmed [ 36 ] and Medhelp [ 37 ]. It determines that the SVM leads over graph-based technique (GBT) and acquires 89%–92% accuracy, whereas SVM gains only 81%–84% accuracy over Pubmed (PM) and Medhelp (MH) dataset, respectively, as shown in Table 6 and Figure 2 .…”

Section: Evaluation Parametermentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

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“…Many studies have linked MP infection to asthma attacks. Therefore, MPP is currently considered to be an important cause of asthma exacerbations in nonasthmatic children [ 5 ]. Studies have shown that the abnormal rate of lung imaging of MPP patient is observably higher in contrast to the positive rate of lung signs.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and Prognostic Analysis of Mycoplasma pneumoniae Pneumonia in Children Based on High‐Resolution Computed Tomography

Leng¹,

Yang²,

Wang³

2022

Contrast Media & Molecular Imaging

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Mycoplasma pneumoniae (MP) is defined as a common cause of pulmonary infections and accounts for up to four over ten of pneumonia in children over age 5. This study was aimed to explore the diagnosis and prognosis of mycoplasma pneumoniae pneumonia (MPP) in children using high-resolution computed tomography (CT) (HRCT). 71 children hospitalized with MPP were undertaken as the research objects to observe the incidence rate, occurrence time, and duration of the clinical symptoms and pathological signs. The chest HRCT and pulmonary ventilation function (PVF) were examined in the acute phase, the second phase re-examination period, and the third phase re-examination period. Relevant indicators were statistically analyzed to determine the change rules of chest HRCT and PVF and correlation between the two. Clinically, the children with MPP suffered from fever, cough, and sore throat. In addition to the above symptoms, children with MPP had different degrees of PVF impairment. Compared with the group with normal HRCT results, the forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), peak expiratory flow (PEF), forced expiratory flow at 25% forced expiratory volume (FEF25), forced expiratory flow at 50% forced expiratory volume (FEF50), forced expiratory flow at 75% forced expiratory volume (FEF75), and maximum mid-expiratory flow (MMEF75/25) of children in bronchopneumonia group, segmental pneumonia group, and lobar pneumonia group were obviously reduced, showing statistically great differences (P < 0.05). Compared with the case in acute phase, the PVF indicators of children in the re-examination phases were much higher, with greatly statistical differences (P < 0.05). In children with MPP, both the large and small airways were affected, but the recovery of the small airways was slow. Pulmonary HRCT and PVF can be undertaken as important indicators to judge the severity and prognosis of MPP in school-age children.

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Chest CT Findings and Differential Diagnosis of Mycoplasma pneumoniae Pneumonia and Mycoplasma pneumoniae Combined with Streptococcal Pneumonia in Children

Cited by 28 publications

References 43 publications

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Diagnosis and Prognostic Analysis of Mycoplasma pneumoniae Pneumonia in Children Based on High‐Resolution Computed Tomography

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