ICKSC :An Efficient Methodology for Predicting Kidney Stone From CT Kidney Image Dataset using Conventional Neural Networks

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Proposed ICKSC architecture
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Published: Dec 31, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i2s.5917
Keywords:
Deep learning, CNN, densenet169, mobilenetv2, vgg19, xception_model

Main Article Content

J. Sarada
Research Scholar, Department of Computer Science, Sri Padmavathi Mahila Vishvavidyalayam (SPMVV), Tirupati, Andhra Pradesh – India
N. V. Muthu Lakshmi
Assistant Professor, Department of Computer Science, Sri Padmavathi Mahila Vishvavidyalayam (SPMVV), Tirupati, Andhra Pradesh – India

Abstract

Chronic Kidney Diseases (CKD) has become one among the world wide health crisis and needs the associated efforts to prevent the complete organ damage. A considerable research effort has been put forward onto the effective separation and classification of kidney Stones from the kidney CT Images. Emerging machine learning along with deep learning algorithms have waved the novel paths of kidney stone detections. But these methods are proved to be laborious and its success rate is purely depends on the previous experiences. To achieve the better classification of kidney stone, this paper proposes a novel Intelligent CNN based Kidney Stone Classification (ICKSC) system which is based on transfer learning mechanism and incorporates 8 Layered CNN, densenet169_model, mobilenetv2_model, vgg19_model and xception_model. The extensive experimentation has been conducted to evaluate the efficacy of the recommended structure and matched with the other prevailing hybrid deep learning model. Experimentation demonstrates that the suggested model has showed the superior predominance over the other models and exhibited better performance in terms of training loss, accuracy, recall and precision.

Article Details

How to Cite
Sarada, J. ., & Lakshmi, N. V. M. . (2022). ICKSC :An Efficient Methodology for Predicting Kidney Stone From CT Kidney Image Dataset using Conventional Neural Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 10(2s), 106–115. https://doi.org/10.17762/ijritcc.v10i2s.5917
Issue
Vol. 10 No. 2s (2022): Computer Science Applications: Innovation and Trends
Section
Articles

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