Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

SAMREEN NAEEM; AQIB ALI; SANIA ANAM

doi:10.3390/mol2net-08-13948

Previous Article in event

Machine Learning Methods Using Texture Feature Selection in Diagnosis of Liver Cancer

Next Article in event

A network pharmacology-based analysis of the main pharmacological pathways of Cardiospermum Halicacabum acting on Human Breast Cancer with computational and experimental validation

Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

SAMREEN NAEEM

^{*

1},

AQIB ALI

¹,

SANIA ANAM

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

Academic Editor: Humbert G. Díaz

Published: 31 December 2022 by MDPI in MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed. congress NICE.XSM-08: North-Ibero-America Congress on Exp. & Simul. Methods, Valencia, Spain-Miami, USA, 2022

https://doi.org/10.3390/mol2net-08-13948

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

View paper

22 Reads
0 Recommendations

Comments on this paper

Iratxe Aguado

3 January 2023

Dear authors thank you for your support to the conference.
Now we closed the publication phase and launched the post-publication phase of the conference. REVIEWWWERS'08 Brainstorming Workshop is now open from 2023-Jan-01 to 2023-Jan-31. MOL2NET Committee, Authors, and Validated Social Media Followers Worldwide are invited to post moderated questions/answers, comments, about papers. Please kindly post your public answers (A) to the following questions in order to promote interchange of scientific ideas. These are my questions (Q) to you:
Q1. Taking into account the General Data Protection Regulation of the European Union, what perspectives are possible when handling different databases in artificial intelligence (AI)?
Q2. What are the most used AI/ML techniques in this field?
Dear author thanks in advance for your kind support answering the questions. Now, please become a verified REVIEWWWER of our conference by making questions to other papers in different Mol2Net congresses. Commenting Steps: Login, Go to Papers List, Select Paper, Write Comment, Click Post Comment. Papers list: https://mol2net-08.sciforum.net/presentations/view,
Workshop link: https://mol2net-08.sciforum.net/#reviewwwers

Samreen Naeem

4 January 2023

Reply1: Artificial intelligence uses intelligent databases (IDB) systems which integrate the resources of both RDBMS's and KB's to offer a natural way to deal with information, making it easy to store, access and apply.

Reply 2: Computed tomography (CT) is a commonly used medical image diagnosis method in clinics.

SAMREEN NAEEM

AQIB ALI

SANIA ANAM