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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14

Issue per Year : 12

Volume Published : 8

Issue Published : 82

Article Submitted : 6414

Article Published : 3411

Total Authors : 8699

Total Reviewer : 545

Total Countries : 75

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Authors Name: Turubati Saikiran , K.Tulasi krishna kumar
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Published Paper Id: IJRTI2303027
Published In: Volume 8 Issue 3, March-2023
Abstract: A respiratory infection brought on by bacteria or viruses, pulmonary emphysema affects a large number of people, especially in developing and underdeveloped countries where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, along with insufficient medical infrastructure. Pleural effusion is a disorder in which fluids fill the lung and make breathing difficult. It is brought on by pulmonary emphysema. To achieve effective treatment and boost survival chances, pulmonary emphysema must be identified early. The most common way to diagnose pulmonary emphysema is by chest X-ray imaging. The analysis of chest X-rays, however, is a difficult task that is vulnerable to subjectivity. We created a method in this work that uses chest X-ray pictures to automatically computer-aided diagnosis detect pulmonary emphysema. To address the lack of available data, we used deep transfer learning and created an ensemble of three convolutional neural network models, GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble strategy was used, and a unique method was used to decide the weights given to the base learners. Precision, recall, f1-score, and area under the curve scores from four common evaluation metrics are used to create the weight vector, which in studies published in the literature was typically set experimentally, an inefficient procedure. Using a five-fold cross-validation method, the suggested method was tested using two pulmonary emphysema X-ray datasets that were made accessible to the public by Kermany et al. and the Radiological Society of North America (RSNA), respectively. Using the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 98.81% and 86.85%, as well as sensitivity values of 98.80% and 87.02%. The outcomes outperformed those of cutting-edge approaches, and our approach outperformed the widely used ensemble techniques. The datasets statistical evaluations using McNemar's and ANOVA tests demonstrated the robustness of the method.
Keywords: Pleural effusion, computer-aided diagnosis, convolutional neural network models, evaluation metrics, robustness, weighted average, precision, robustness, the Radiological Society of North America (RSNA).
Cite Article: "PYTHON BASED PREDICTION OF PULMONARY EMPHYSEMA", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.8, Issue 3, page no.161 - 173, March-2023, Available :
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2303027
Registration ID:185465
Published In: Volume 8 Issue 3, March-2023
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Page No: 161 - 173
Country: Visakhapatnam, Andhra Pradesh, India
Research Area: Master of Computer Application 
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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