OPTIMIZATION OF NODE SIZE CONFIGURATION IN CNN-ELM MODEL FOR BRAIN TUMOR MRI IMAGE CLASSIFICATION
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5581Abstract
This study proposed a method to classify four types of brain tumors—Glioma, Meningioma, Pituitary, and Non-Tumor—using the Kaggle Brain Tumor MRI Dataset. The research involved stages of data collection, preprocessing, model design, model training, and evaluation. A hybrid Convolutional Neural Network - Extreme Learning Machine (CNN-ELM) algorithm was employed, demonstrating the importance of selecting the optimal number of hidden nodes for achieving high accuracy. The test results revealed that with 2000 hidden nodes, the CNN-ELM model achieved an overall accuracy of 98.86%, with F1-scores of 97% for Glioma, 98% for Meningioma, 100% for Non-Tumor, and 100% for Pituitary tumors. In comparison, the model with 1000 hidden nodes achieved an accuracy of 96.96%, while models with 3000 and 4000 hidden nodes achieved 98.10% and 96.58% accuracy, respectively. These findings highlight the critical role of hidden node selection in optimizing model performance. The CNN-ELM algorithm proves to be a viable alternative for classifying brain tumor MRI images, contributing to advancements in medical technology.
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