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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Detection of Pneumonia using Fuzzy Expert System |
| Authors Name: | Rakshitha B , Sanjana P Rao , Sanjana T M , Shilpashree B C , Dr. Manu A P |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_212500
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| Published Paper Id: | IJNRD2404776 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Respiratory diseases, especially pneumonia, are a global health problem that still requires accurate and effective diagnosis. The system uses an expert fuzzy system to integrate key measurements such as body temperature, sputum, sputum color, chest pain, dyspnea(shortness of breath), respiratory rate, heart rate, systolic blood pressure and white blood count. These parameters form the basis of a good decision-making model and reflect the complexity of pneumonia diagnosis. Moreover, to improve diagnostic accuracy, our system includes a chest X-ray image processed by convolutional neural networks (CNN), including models such as VGG16 and ResNet50V2. This bimodal approach is designed to use both quantitative clinical data and qualitative data, allowing efficient and accurate assessment of pneumonia pressence. Fuzzy expert systems use fuzzy logic to model uncertainty in diagnosis and provide a flexible model for fuzzy thinking. The integration of image processing technology not only makes it easier to extract X-ray images from X-ray, but also creates hybrid decisions. This combination gives the percentage chance of getting pneumonia to help doctors make decisions. The proposed model was validated using different datasets. Performance measurements including sensitivity, specificity, and accuracy demonstrate the effectiveness of our method compared to traditional methods. The integration of clinical and image-based features makes the system reliable and useful in early and accurate diagnosis of pneumonia diagnosis. This study bridges the gap between traditional clinical examination and new technology, laying the foundation for a more nuanced and sophisticated way to diagnose pneumonia diagnosis. |
| Keywords: | Pneumonia, fuzzy expert system, CNN, VGG16, ResNet50V2, X-Ray image. |
| Cite Article: | "Detection of Pneumonia using Fuzzy Expert System", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h717-h721, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404776.pdf |
| Downloads: | 00035 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2404776 Registration ID: 212500 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: h717-h721 Country: Shivamogga, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404776 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404776 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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