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An Optimized Transfer Learning Model Based Kidney Stone Classification

S. Devi Mahalakshmi*

Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, 626005, Tamilnadu, India

* Corresponding Author: S. Devi Mahalakshmi. Email: email

Computer Systems Science and Engineering 2023, 44(2), 1387-1395.


The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processing-based diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet applied for classification. The combined classification results are processed by ensemble learning to increase classification performance. The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer (GTO). The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.


Cite This Article

APA Style
Mahalakshmi, S.D. (2023). An optimized transfer learning model based kidney stone classification. Computer Systems Science and Engineering, 44(2), 1387-1395.
Vancouver Style
Mahalakshmi SD. An optimized transfer learning model based kidney stone classification. Comput Syst Sci Eng. 2023;44(2):1387-1395
IEEE Style
S.D. Mahalakshmi, "An Optimized Transfer Learning Model Based Kidney Stone Classification," Comput. Syst. Sci. Eng., vol. 44, no. 2, pp. 1387-1395. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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