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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
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Total Authors : 22851
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| Paper Title: | Pneumonia Detection Using CNN-based Feature Extraction |
| Authors Name: | J. Siva Sankar Babu , K. Sai Sriya , T. Harshitha , T. Vyshnavi , K. Charan Raj, C. Ajay Kumar |
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IJRTI_189248
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| Published Paper Id: | IJRTI2403040 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumonia. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analysing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analysing chest X-ray images, specifically to detect Pneumonia. Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumonia. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analysing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analysing chest X-ray images, specifically to detect Pneumonia. |
| Keywords: | Keywords: Data Augmentation, Deep Convolutional Neural Networks, Feature extraction, Classification. |
| Cite Article: | "Pneumonia Detection Using CNN-based Feature Extraction", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.9, Issue 3, page no.271 - 277, March-2024, Available :http://www.ijrti.org/papers/IJRTI2403040.pdf |
| Downloads: | 000205247 |
| 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: IJRTI2403040
Registration ID:189248
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: 271 - 277 Country: krishna, Andhra Pradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2403040 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2403040 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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