EventsMOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed.
Published
This submission belongs to the session 05. NICE.XSM-08: North-Ibero-America Congress on Exp. & Simul. Methods, Valencia, Spain-Miami, USA, 2022 of the event MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed.
Published date
31 Dec, 2022
Academic Editor
author-avatarHumbert G. Díaz
Citation
SAMREEN NAEEM, AQIB ALI, SANIA ANAM, Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach, in Proceedings of MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed., 1 January–15 January 2023, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-08-13948
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Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

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SANIA ANAM 3
1. Southeast University, Nanjing, China, Pakistan
2. Southeast University, Nanjing, China
3. Department of Computer Science, Govt Associate College for Women Ahmadpur East, Bahawalpur, Pakistan.
Abstract

Nephrolithiasis, commonly known as kidney stone disease, is a disorder in which the deposition of certain minerals causes a stone to develop in the urinary tract. Urolithiasis is another name for nephrolithiasis. Most of the time, kidney calculi form in the renal system and are eliminated through the urine system. Even a tiny stone can readily travel through the urine without any issues. More than 5 millimeters (0.2 inches) in diameter, fully grown calculi can clog the urinary tract, which can cause intense pain in the lumbar region or the stomach. Calculi can result in problems such as dysuria, vomiting, and hematuria. The recommended approach for automatically segmenting kidney stones is based on a four-stage framework, the first of which calls for pre-processing kidney pictures for better enhancement and is followed by the active contour method for automatically segmenting kidney stones. In the future, an assessment will be performed depending on the size and kind of stone.

Keywords
Nephrolithiasis
Ultrasound
Machine Learning
Classification
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