CLASSIFICATION OF RESPIRATORY DISORDERS USING SEGMENTATION, FEATURE EXTRACTION AND FEATURE FUSION OF X-RAY IMAGES

Registration ID: IJNRD_192025

Published ID: IJNRD2304436

DOI: Click Here to Get

Naveen Kumar N , Manjunath D , Manoj D Bhat , Mani J S

Segmentation, Feature Extraction, and Feature Fusion, Classification, UNet.

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.

Published Paper   E-Certificate

Naveen Kumar N , Manjunath D, Manoj D Bhat, & Mani J S (April-2023). CLASSIFICATION OF RESPIRATORY DISORDERS USING SEGMENTATION, FEATURE EXTRACTION AND FEATURE FUSION OF X-RAY IMAGES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e269-e275. https://ijnrd.org/papers/IJNRD2304436.pdf

Volume 8 Issue 4, April-2023

Pages : e269-e275

Paper Reg. ID: IJNRD_192025

Published Paper Id: IJNRD2304436

Downloads: 000121975

Research Area: Engineering

Country: BANGALORE, KARNATAKA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304436.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304436

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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