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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Authors
Kanupuri Baby Kumari
Downlaod Center
Keywords
Kidney disease poses a significant health challenge, with kidney stones being a common manifestation that requires timely diagnosis and treatment. Traditional diagnostic methods often rely on radiological imaging, which can be time-consuming and subjective, heavily reliant on the expertise of radiologists. This research introduces a novel approach to automate the detection of kidney stones using deep learning techniques. In this study, a diverse dataset of medical images, encompassing normal kid
Abstract
Kidney disease poses a significant health challenge, with kidney stones being a common
manifestation that requires timely diagnosis and treatment. Traditional diagnostic methods often
rely on radiological imaging, which can be time-consuming and subjective, heavily reliant on the
expertise of radiologists. This research introduces a novel approach to automate the detection of
kidney stones using deep learning techniques.
In this study, a diverse dataset of medical images, encompassing normal kidney tissue, kidney
stones, cysts, and tumors, is collected and annotated. Two state-of-the-art deep learning
architectures, VGG16 and ResNet50, are employed for the classification task, leveraging their
ability to extract intricate details from medical images.
The methodology involves data preprocessing, including dataset balancing to prevent class
imbalance bias. Feature selection techniques are applied to improve classification accuracy by
removing irrelevant and redundant attributes. Convolutional Neural Networks (CNNs) are utilized,
comprising convolution, pooling, and fully connected layers, to identify patterns in the images and
classify them into the relevant categories.
Results demonstrate the efficacy of the proposed deep learning models, achieving an impressive
accuracy rate of 99.36% in the classification of kidney stones, cysts, tumors, and normal kidney
tissue. Confusion matrix analysis further validates the model's accuracy.
The outcomes of this research indicate that deep learning models can significantly expedite the
diagnostic process for kidney disease, reduce healthcare costs, and potentially lead to earlier
diagnoses and treatments. Moreover, the burden on radiologists can be alleviated through the
deployment of a fully developed automated system for kidney illness detection.
IJCRT's Publication Details
Unique Identification Number - IJCRT2309217
Paper ID - 243934
Author type - Indian Author
Page Number(s) - b825-b831
Pubished in - Volume 11 | Issue 9 | September 2023
DOI (Digital Object Identifier) -
No Of Downloads - 140
Author Country - India, 530003, Visakhapatnam, Visakhapatnam, 530003, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2309217
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Kanupuri Baby Kumari, "KIDNEY DISEASE DETECTION USING DEEP LEARNING MODEL", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 9, pp.b825-b831, September 2023, Available at :http://www.ijcrt.org/papers/IJCRT2309217.pdf
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