What Are The Types Of Pneumonia – A powerful paradigm for cardiovascular risk stratification using multiclass, multivariate, and ensemble machine learning paradigms: a narrative review
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What Are The Types Of Pneumonia
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Four types of multi-class frameworks for pneumonia classification and their validation in X-ray scans using seven types of deep artificial intelligence models
A Deep Learning Pipeline For The Diagnosis And Discrimination Of Viral, Non Viral And Covid 19 Pneumonia From Chest X Ray Images
By Nilmani 1, Pankaj K. Jain 1, Neeraj Sharma 1, Mannudeep K. Kalra 2, Claudia Viskovic 3, Luka Saba 4 and Jasjit S. Suri 5, 6, *
Received: February 21, 2022 / Revised: March 4, 2022 / Accepted: March 4, 2022 / Published: March 7, 2022
Background and motivation: The novel coronavirus that causes COVID-19 is highly contagious, mutative, and devastating human health and life as well as the global economy through the constant evolution of new variants and harmful outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has important limitations. In addition, multi-class classification systems for X-ray lungs that include viral, bacterial, and tuberculosis classes, including COVID-19, are unreliable. Therefore, a robust, rapid, cost-effective and easily accessible diagnostic method is needed. Methodology: Artificial Intelligence (AI) has been shown to revolutionize all areas of life, especially medical imaging. This study presents an accessible and very cost-effective deep learning-based automatic multi-class detection and classification of pneumonia from chest X-ray images. The study designed and applied seven highly efficient pre-trained neural networks, including 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 grades two, three, and five. The best results were using 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%; and AUC 1.0, 0.97, 0.92, respectively (p < 0.0001 for all). Our system outperformed the existing methods of the five-year model by 1.2 percent. The online system takes <1 second while demonstrating reliability and stability. Conclusion: AI deep learning is a powerful paradigm for multi-class classification of pneumonia.
COVID-19 is a highly contagious disease caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2) . The virus was first isolated in December 2019 in Wuhan, China from three pneumonia patients with severe respiratory disease . In a short time, the virus spread all over the world. On March 11, 2020, the World Health Organization (WHO) declared this disease a pandemic . Coronaviruses (CoV) are an extremely diverse family of positive enveloped single-stranded RNA viruses . Viruses are highly pathogenic and transmissible viruses that spread between close individuals through respiratory droplets or aerosols , resulting in multiple pathways  affecting various organs such as the heart  and liver . , which cause diabetes.  and pulmonary embolism [10, 11]. In most cases of infection, a person begins to develop symptoms such as cough, fever, fatigue, and loss of smell or taste. In many fatal cases, the infection spreads to the lower respiratory system, including the lungs, causing diseases such as acute pneumonia, followed by multiorgan dysfunction syndrome with various secondary infections and shock [12, 13, 14, 15, 16, 17]. .
Diagram Showing Types Pneumonia Illustration Stock Vector Image & Art
Even two years after the outbreak and nearly 10 billion doses of the vaccine, the disease continues to wreak havoc on human health, lives, and the global economy. Viruses are extremely efficient at mutating rapidly and gradually transforming into more lethal variants . After the Delta variant was destroyed, a new variant named Omicron was discovered. WHO has already designated Omicron as a variant of concern . Several significant mutations in the Omicron spike proteins make it highly transmissible. In addition, since then there is still the risk of new mutations in Cov-2, making it possible for harmful variants to emerge.
COVID-19 infection is usually diagnosed by polymerase chain reaction (RT-PCR) testing, often followed by chest radiography such as X-ray and computed tomography (CT) [20, 21]. The reference method for detecting COVID-19 is RT-PCR; although this process is laborious, complex, rigorous and time-consuming with a very high error rate [20, 22, 23]. The RT-PCR kit is expensive along with a special biological facility to house the PCR machine. As a result, there is a significant supply constraint. Many nations are facing false positive cases of COVID-19 caused by insufficient supply of test kits as well as delays in test results. These limitations of RT-PCR pose significant obstacles to limiting disease surveillance as infection spreads among healthy populations [ 24 ].
To combat the spread of COVID-19, patients must undergo prompt and effective surveillance and receive appropriate medical care. Various medical imaging techniques, such as chest x-ray (CXR) and computed tomography (CT), can help in this task [25, 26]. COVID-19 was recently detected by CT [25, 27], but high patient dose and screening costs are the main drawbacks of using CT for diagnosis . On the other hand, CXR equipment is commonly available in hospitals and diagnostic centers to quickly and inexpensively create a 2D projection of the chest. Radiologists already use the CXR technique to detect chest abnormalities in various lung diseases, including pneumonia and tuberculosis. Detection of COVID-19 has also been performed using CXR in some patients [25, 29]. Patients with COVID-19 show similar radiographic findings such as bilateral, peripheral and basal-dominant ground-glass opacities, septal thickening, pleural effusion, bronchiectasis, and bilateral lymphadenopathy [27, 30, 31, 32, 33, 34, 34]. As a result, CXR scans can help in early detection of COVID-19 in a suspected person. However, one problem is that the CXRs of different pneumonias are very similar; therefore, it is difficult to manually distinguish COVID-19 from other lung disorders. However, deep learning algorithms powered by artificial intelligence (AI) can effectively extract various image-based features that radiologists cannot observe manually in the original CXR. In terms of image extraction and classification, convolutional neural networks (CNN) have proven their effectiveness and are widely applied by 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 decisions are used to solve various health problems, such as brain tumor detection [57, 58, 59], lung and breast cancer detection [60, 61, 62], Alzheimer’s disease diagnosis , heart and veins are widely used. disease predictions [64, 65, 66, 67, 68, 69, 70], detection of pneumonia [71, 72, 73, 74, 75] and many others. With promising results in various applications, deep learning methods for chest radiography are gaining popularity in recent years. Transfer learning technology has facilitated the fast processing of very deep CNNs [76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87].
In this work, we designed and implemented seven different deep learning models using a transfer learning method for multi-class detection of COVID-19 in CXR images. We performed binary and multi-class classification of COVID-19 and other lung diseases, i.e., viral pneumonia (VP), bacterial pneumonia (BP), tuberculosis (TB), and normal images. Then, we compared the results to get the most suitable model for its usefulness in practice. Figure 1 shows a general schematic diagram of the development of the COVID-19 detection system.
Recurrent Or Persistent Pneumonia
All the work is organized by sections. In section 2, we reviewed all related works and contributions of different authors in this area. Section 3 explains datasets, image preprocessing, and deep learning models. Section 4 presents experimental results and their comparative indicators. Chapter 5 deals with evaluating the effectiveness of the models. Section 6 then presents the empirical validation of the proposed models on other data sets. Furthermore, in Section 7, we compared the proposed models with other state-of-the-art methods. Finally, Chapter 8 concludes the study and introduces itself