Hyperparameter Tuning Exploration to Maximize MobileNet Performance in Classification Kidney Tumor - Siregar, Sandy Putra; Safii, M.; Andani, Sundari Retno (CogITo Smart Journal, 2025)
The main focus in this research is how to develop the MobileNet architecture in order to produce a kidney tumor classification model that is accurate, resistant to overfitting, and remains consistent with variations in datasets and training parameters. This study aims to develop MobileNet architecture to produce a software model that can precisely identify with high accuracy, perform kidney tumor classification, and avoid failure in generalizing new data called overfitting, as well as evaluate the difference in accuracy generated from several variations of datasets and parameters. The method used in this study is MobileNet with hyperparameter tuning and fine-tuning, and it was compared with the MobileNet Baseline method. The dataset consists of 12,446 images classified as Normal, Cyst, Stone, and Tumor, collected from Kaggle. The findings of this study on the division of the 80:10:10 ratio of the proposed method image data resulted in 100% accuracy, 100% precision, 100% recall, and 100% F1-Score. This study is expected to produce architecture modifications that can classify kidney tumors with high accuracy so that the hypothesis is achieved. In addition, various approaches in medical image analysis using deep learning have shown better results in identifying various tumors, especially this research in the classification and detection of kidney tumors.