Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques

Rudi Setiawan; Andi Maulidinnawati Abdul Kadir Parewe; Asslia Johar Latipah; Nur Rochmah Dyah Puji Astuti; Aris Wahyu Murdiyanto; Fajri Profesio Putra

doi:10.56705/ijaimi.v1i2.100

Authors

Rudi Setiawan Universitas Trilogi
Andi Maulidinnawati Abdul Kadir Parewe Universitas Teknologi Akba Makassar
Asslia Johar Latipah Universitas Muhammadiyah Kalimantan Timur
Nur Rochmah Dyah Puji Astuti Universitas Ahmad Dahlan
Aris Wahyu Murdiyanto Universitas Jenderal Achmad Yani Yogyakarta
Fajri Profesio Putra Politeknik Negeri Bengkalis

DOI:

https://doi.org/10.56705/ijaimi.v1i2.100

Keywords:

Bagging-meta Estimator, CT Kidney Disease Classification, Sobel Segmentation, Hu Moment Feature Extraction, Imbalanced Dataset, Machine Learning

Abstract

This study investigates the efficacy of the Bagging-meta estimator in classifying CT kidney diseases, focusing on an imbalanced dataset processed through Sobel segmentation and Hu moment feature extraction. The research utilized a quantitative approach, applying the Bagging-meta estimator to a dataset comprising CT images classified into four categories: Normal, Cyst, Tumor, and Stone. These images were preprocessed using Sobel segmentation to highlight critical structures and Hu moment feature extraction for robust classification features. The study employed a 5-fold cross-validation method to evaluate the model's performance, assessing metrics such as accuracy, precision, recall, and F1-Score. The results indicated a significant variation in the model's performance across different folds, with accuracy ranging from 49.86% to 66.17%, precision between 51.86% and 65.93%, recall from 57.95% to 64.44%, and F1-Scores spanning 48.26% to 60.74%. These findings suggest that while the Bagging-meta estimator can achieve reasonable accuracy in classifying kidney diseases from CT images, its performance is affected by the imbalanced nature of the dataset. This study contributes to the understanding of the challenges and potential of machine learning in medical imaging, particularly in the context of imbalanced datasets. It highlights the need for specialized approaches to handle such datasets and underscores the importance of preprocessing techniques in enhancing model performance. Future research directions include exploring methods to address data imbalance, investigating alternative feature extraction techniques, and testing the model on diverse datasets to enhance its generalizability and reliability in clinical settings. This research offers valuable insights into the development of automated diagnostic tools in medical imaging and advances the field of computer-aided diagnosis in nephrology.

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