Evaluation and Classification of Kidney Stone Detection Using Deep Learning Techniques-Reference-Cited by-同舟云学术

Evaluation and Classification of Kidney Stone Detection Using Deep Learning Techniques

Published:2023-12-22 Issue: Volume:11 Page:01-06
ISSN:
Container-title:2023 6th International Conference on Software Engineering and Computer Science (CSECS)
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Author:

Nadeem Muhammad¹,Tan Guozhen¹,Altaf Muhammad²,Mumtaz Muhammad Khalid¹,Fatima Aiman³,Noshi ⁴

Affiliation:

1. Dalian university of Technology,Computer Science and Technology,Dalian,China

2. School of Information and Communication, Dalian University of Technology),Dalian,China

3. Ghazi University,Computer Science and Technology,Dera Ghazi Khan,Pakistan

4. School of Software, Dalian University of technology,Dalian,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10428115/10428104/10428612.pdf?arnumber=10428612

Reference12 articles.

1. Kidney Stone Disease: An Update on Current Concepts

2. The effect of selectivity and half-life of nonsteroidal anti - inflammatory drugs for the development of subclinical kidney damage;Mustafayeva;Finland International Scientific Journal of Education, Social Science & Humanities,2023

3. NeuroTrust—Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things

4. Image-based geometric digital twinning for stone masonry elements

5. Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach

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1. Kidney Disease Classification and Diagnosis: A Comprehensive Review of Current AI Techniques;2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA);2024-08-06

2. Enhancing Kidney Stone Diagnosis: A Fusion Approach of FCM and CNN for Precise Detection;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26