What Are The Four Types Of Pneumonia – A Powerful Paradigm for Cardiovascular Risk Stratification Using a Multiclass, Multilabel, and Ensemble-Based Machine Learning Paradigm: A Narrative Review
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What Are The Four Types Of Pneumonia
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Table 3 From Bronchoalveolar Lavage Fluid Cellular And Haematological Changes In Different Types Of Caprine Pneumonia.
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Four types of multi-class framework and validation for classification of pneumonia in X-ray scans using seven types of deep learning artificial intelligence models
Things To Know About Pneumonia
Nillmani 1, Pankaj K. Jain 1, Neeraj Sharma 1, Mannudeep K. Kalra 2, Klaudija Viskovic 3, Luca Saba 4 and Jasjit S. Suri 5, 6, *
Received: February 21, 2022 / Revised: March 4, 2022 / Received: March 4, 2022 / Posted: March 7, 2022
Background and Motivation: The novel coronavirus that causes COVID-19 is exceptionally contagious and highly mutable, resulting in a decline in human health and life as well as the global economy through the development of new harmful strains and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has serious limitations. Also, multi-class lung classification x-ray systems with viral, bacterial, and tuberculosis classes, including COVID-19, are unreliable. Therefore, a diagnostic method that is robust, rapid, cost-effective, and readily available is needed. How: Artificial intelligence (AI) has proven to revolutionize all layers, especially medical imaging. This study proposes an easily available and highly cost-effective automatic multi-grade detection and classification of pneumonia based on AI deep learning from chest X-ray images. This study designed and applied seven highly efficient pretrained convolutional neural networks: VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152 to classify up to five classes of pneumonia. Results: The database consisted of 18,603 scans with 2, 3 and 5 classes. The best results were with DenseNet201, VGG16, and VGG16 with accuracies of 99.84%, 96.7%, and 92.67%, respectively. Sensitivity 99.84%, 96.63%, 92.70%; Specificity 99.84, 96.63%, 92.41%; AUCs were 1.0, 0.97, and 0.92, respectively (all p < 0.0001). Our system outperformed the conventional method by 1.2% for the 5-class model. The online system lasts less than a second, demonstrating reliability and stability. Conclusion: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.
COVID-19 is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)[1]. The virus was first isolated in December 2019 from three pneumonia patients with severe respiratory illness in Wuhan, China [2]. In a short time, the virus has spread all over the world. On March 11, 2020, the World Health Organization (WHO) declared the disease a pandemic [3]. Coronaviruses (CoVs) are a highly diverse family of enveloped, positive-sense single-stranded RNA viruses [4]. This virus is a highly pathogenic and contagious virus that spreads through respiratory droplets or aerosols between close individuals [5], leading to multiple routes [6] and causing damage to various organs such as the heart [7] and liver [8] [8]. ] diabetes . [9] and pulmonary embolism [10, 11]. In most cases of infection, a person begins to show symptoms such as cough, fever, fatigue, and loss of smell or taste. In fatal cases, the infection progresses to the lower respiratory tract, including the lungs, causing diseases such as severe pneumonia, followed by multiple organ dysfunction syndromes accompanied by multiple secondary infections and shock in many cases [12, 13, 14, 15, 16, 17 ]. .
What Is Pneumonia? Symptoms, Causes, Types And Treatment
After two years of outbreaks and nearly 10 billion doses of vaccines administered, the disease continues to wreak havoc on human health, lives and the global economy. The virus is very efficient at mutating rapidly and gradually converting to more virulent strains [18]. After the severe damage of the Delta strain, a new strain called Omicron was discovered. WHO has already designated Omicron as a concern [19]. It is highly contagious due to several notable mutations in the Omicron spike protein. There is also a risk of developing more new mutations in Cov-2 later, potentially resulting in more deleterious variants.
Infection with COVID-19 is usually detected by a reverse transcriptase polymerase chain reaction (RT-PCR) test, often followed by chest radiographs such as X-rays and computed tomography (CT) scans [20, 21] . The reference technique for detecting COVID-19 is RT-PCR. However, the procedure is laborious, complex, rigorous, time-consuming, and has a fairly high error rate [20, 22, 23]. RT-PCR kits are expensive, with specific biosafety facilities to house the PCR machines. Therefore, supply constraints are severe. Many countries are struggling with false positive cases of COVID-19 due to supply shortages and delayed test results. These limitations of RT-PCR present a major obstacle to limiting disease control as infections spread among healthy populations [24].
To combat the spread of COVID-19, patients need prompt and effective screening and appropriate treatment. Several medical imaging modalities can help with this, including chest x-ray (CXR) and computed tomography (CT) [25, 26]. COVID-19 has recently been detected with CT images [25, 27], but the disadvantages of using CT images for diagnosis include high patient dose and cost of examination [28]. CXR machines, on the other hand, are commonly accessed by hospitals and diagnostic centers to quickly and inexpensively create 2D projections of the chest. Radiologists already use CXR methods to detect chest abnormalities in a variety of lung diseases, including pneumonia and tuberculosis. COVID-19 detection has been performed using CXR in some patients [25, 29]. Patients with COVID-19 present similar findings on radiographs, such as bilateral, peripheral and basolateral predominant ground glass opacities, septal thickening, pleural effusion, bronchiectasis, and bilateral lymphadenopathy [27, 30, 31, 32, 33, 34; 35]. As a result, CXR scans can help detect COVID-19 in a suspected person early. One problem, however, is that the CXRs of various pneumonias are very similar. Therefore, it is difficult to distinguish COVID-19 from other lung abnormalities by hand. However, deep learning algorithms powered by artificial intelligence (AI) can effectively extract many image-based features that radiologists cannot manually observe in the original CXR. In terms of image feature extraction and classification, convolutional neural networks (CNNs) have proven effective and are widely implemented in the research community [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 , 48, 49, 50, 51, 52, 53, 54, 55, 56]. Currently, CNN-based solutions are widely used to solve various health problems, such as brain tumor identification [57, 58, 59], lung and breast cancer diagnosis [60, 61, 62], Alzheimer’s disease diagnosis [63], and cardiovascular. disease prediction [64, 65, 66, 67, 68, 69, 70], pneumonia detection [71, 72, 73, 74, 75], etc. Deep learning techniques for chest X-rays have gained prominence in recent years with promising results in several applications. Transfer learning techniques facilitate fast retraining of very deep CNNs [76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87], making the task smoother.
In this work, we designed and applied seven different deep learning models using transfer learning method to detect multi-class COVID-19 in CXR images. We performed binary and multiclass classification with images of COVID-19 and other pulmonary diseases: viral pneumonia (VP), bacterial pneumonia (BP), tuberculosis (TB), and normal images. The results were then compared to obtain the model that best suited its practical usefulness. Figure 1 shows the overall schematic of the development of the COVID-19 detection system.
Pneumonia Nursing Care Plans
The entire work was organized into sections. In Section 2, we explored all relevant work and contributions by other authors in this field. Section 3 describes the dataset, image preprocessing, and deep learning model. In Chapter 4, the experimental results and comparative performance were presented. Chapter 5 deals with model performance evaluation. Then, in Section 6, we present scientific validation of the proposed model performed on different datasets. Also, in Chapter 7, the proposed model was compared with other state-of-the-art methods. Finally, Chapter 8 concludes the research and presents the following.