|
|||||||||||||||
|
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) |
||||||||||||||
Issue: May 2024
Volume 9 | Issue 5
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 96
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | CLASSIFICATION OF RESPIRATORY DISORDERS USING SEGMENTATION, FEATURE EXTRACTION AND FEATURE FUSION OF X-RAY IMAGES |
| Authors Name: | Naveen Kumar N , Manjunath D , Manoj D Bhat , Mani J S |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_192025
|
| Published Paper Id: | IJNRD2304436 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Understanding lung disease and its consequences is one of the best research in recent years. Along with the many uses of medical images in hospitals, pathologies and laboratories, the scope of medical image data has also expanded to capture hospital disease. Although a great deal of research has been done on this particular topic, area is still confusing and complex. There are many ways to classify medical images in the literature. The main disadvantage of traditional processing is the difference between low-level image information captured by imaging equipment and high-level information seen by humans. Respiratory diseases are also one of the leading infectious diseases responsible for deaths in children under the age of five. Early diagnosis is important in lung disease. Many image processing and machine learning models have been developed for this purpose. Different types of existing deep learning methods were used for lung disease predictions, including neural network (CNN), vanilla neural network, group view geometry-based neural network (VGG). Basic CNNs do not perform well in rotated, tilted, or other different image orientations. Therefore, we combine by, segmentation and data augmentation with CNNs to propose a new hybrid deep learning framework. The Lung X-ray was key in diagnosing this disease. In this project, we use deep learning as a combination of image segmentation and feature merging. We use the U-NET architecture for the segmentation model. We tried different pre-trained models for classification. We also use image magnification techniques to resolve inconsistencies and increase generality in the dataset. In this project, we use deep learning to identify respiratory diseases. We use image segmentation to accurately quantify more than lung regions, then we first extract maps from the CNN model, then do the fusion and finally classify the information. First, the segmentation is used to complete the image segmentation, which will be divided into lung regions. Feature Fusion is an algorithm that can combine separate features into features for easy operation of features. |
| Keywords: | Segmentation, Feature Extraction, and Feature Fusion, Classification, UNet. |
| Cite Article: | "CLASSIFICATION OF RESPIRATORY DISORDERS USING SEGMENTATION, FEATURE EXTRACTION AND FEATURE FUSION OF X-RAY IMAGES", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e269-e275, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304436.pdf |
| Downloads: | 000118749 |
| 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 |
| Publication Details: |
Published Paper ID:IJNRD2304436 Registration ID: 192025 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: e269-e275 Country: BANGALORE, KARNATAKA, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304436 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304436 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn