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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
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| Paper Title: | Comparative Analysis of Chest X-ray Classification Models by using ResNet-50 vs. VGG16 vs. VGG19 |
| Authors Name: | Sibasish Patra , Dr. Tapas Kumar Patra |
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IJRTI_190000
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| Published Paper Id: | IJRTI2406030 |
| Published In: | Volume 9 Issue 6, June-2024 |
| DOI: | |
| Abstract: | The emergence of the novel coronavirus disease has become a significant global public health concern, resulting in significant strain on both society and healthcare systems worldwide. The increasing numbers of COVID cases and fatalities have further intensified this strain. However, one potential strategy to alleviate this stress is through the rapid diagnosis of the disease, which is crucial for combating its spread. Unfortunately, conventional diagnostic methods often involve lengthy procedures. Utilizing technologies such as computer-aided design systems can expedite this process, particularly through the analysis of medical images like chest X-rays. Hence, this study aims to develop a computer-aided design system specifically tailored for the classification of chest X-rays into two categories: COVID-19 and Normal. To achieve this goal, we explore the utilization of two pre-trained neural networks to determine the most effective approach for our system. The paper investigates the comparison of various neural network models for binary classification tasks involving normal contrast COVID-19 chest X-rays. Specifically, it compares the performance of VGG16, VGG19, and ResNet50 architectures in terms of accuracy, F1 score, and recall using chest X-ray images categorized as normal and COVID-affected images. Experiments were conducted under two modes: Training and Testing the data, with 80% of the data used for training and the remaining 20% for testing purposes. Compared with other models, VGG16 demonstrates superior performance among the evaluated architectures for chest X-ray classification. For normal contrast images, VGG16 achieved accuracies of 0.94, while VGG19 and ResNet50 attained 0.89 and 0.81, respectively. Thus, it can be concluded that VGG16 achieved the highest accuracy compared to ResNet50 and VGG19. These results underscore the efficacy of VGG16 for medical image classification tasks, particularly in scenarios involving neural network models. The study provides valuable insights for optimizing neural network architectures in medical chest X-ray imaging applications, emphasizing the importance of model selection and data interpretation strategies in enhancing classification accuracy. |
| Keywords: | Deep learning, Covid-19, VGG-19, X-ray |
| Cite Article: | "Comparative Analysis of Chest X-ray Classification Models by using ResNet-50 vs. VGG16 vs. VGG19", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 6, page no.219 - 224, June-2024, Available :http://www.ijrti.org/papers/IJRTI2406030.pdf |
| Downloads: | 000205092 |
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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 |
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Published Paper ID: IJRTI2406030
Registration ID:190000
Published In: Volume 9 Issue 6, June-2024
DOI (Digital Object Identifier):
Page No: 219 - 224 Country: Bhubaneswar , odisha, india Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2406030 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2406030 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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