OPTIMIZED MODEL FOR KIDNEY DISEASE CLASSIFICATION USING FINE-TUNED VGG19 VISION TRANSFORMERS AND BAYESIAN OPTIMIZATION
DOI:
https://doi.org/10.29121/shodhkosh.v7.i13s.2026.8434Keywords:
Kidney Disease, Deep Learning, Vision Transformer, Bayesian Optimization, Medical Imaging ProcessAbstract [English]
Kidney disease is a significant global health concern, affecting millions of individuals annually, with its early detection playing a pivotal role in reducing mortality and improving patient outcomes. Traditional diagnostic approaches rely heavily on manual analysis by medical experts, which is often time-intensive and prone to subjectivity. The emergence of advanced deep learning models offers promising alternatives, yet many existing methods struggle with achieving high accuracy and generalizability due to suboptimal model configurations or reliance on limited datasets. To address this gap, this study proposes a fine-tuned VGG19 model optimized using a Grid Search algorithm to enhance kidney disease classification performance using CT scan images. A Grid Search optimization process was applied to fine-tune hyperparameters to maximize model performance. The experimental results demonstrate that the proposed model achieves superior performance metrics, including an accuracy of 98.78%, precision of 98.84%, recall of 98.28%, and an F1-score of 98.55%, outperforming established models like DenseNet201, EfficientNetB0, and SSLSD-KTD. This study provides a significant contribution to kidney disease classification, demonstrating the efficacy of combining fine-tuned deep learning architectures with systematic hyperparameter optimization.
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