UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 11
November-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2311084


Registration ID:
527448

Page Number

a675-a679

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Title

Kidney Stone Detection with Deep Learning: A review

Abstract

Kidney stones, a common urological condition, pose significant health risks if left untreated. This synopsis introduces a project focused on developing a precise kidney stone detection system using ultrasound images and advanced machine learning techniques, specifically Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), utilizing a dataset of ultrasound images. Our methodology includes ultrasound image pre-processing to enhance quality and CNNs for automatic pattern extraction. CNNs are well-suited for medical image analysis due to their feature extraction capabilities. Concurrently, SVMs improve region classification for increased accuracy. The successful completion of this project has the potential to transform kidney stone diagnosis, providing a non-invasive, cost-effective, and accurate tool for healthcare professionals. This project underscores the interdisciplinary role of artificial intelligence in healthcare, offering opportunities for advancements in disease detection and diagnosis.

Key Words

Kidney Stones, Ultrasound images, Machine learning, Deep Learning, Support Vector Machines (SVM), Convolution Neural Networks (CNN), Medical image analysis

Cite This Article

"Kidney Stone Detection with Deep Learning: A review ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a675-a679, November-2023, Available :http://www.jetir.org/papers/JETIR2311084.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Kidney Stone Detection with Deep Learning: A review ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa675-a679, November-2023, Available at : http://www.jetir.org/papers/JETIR2311084.pdf

Publication Details

Published Paper ID: JETIR2311084
Registration ID: 527448
Published In: Volume 10 | Issue 11 | Year November-2023
DOI (Digital Object Identifier):
Page No: a675-a679
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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