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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Lu’lu’a, Na’imatul
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Computer Science & IT Education Engineering

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Optimizing Brain Tumor MRI Classification with Transfer Learning: A Performance Comparison of Pre-Trained CNN Models Mardianto, M. Fariz Fadillah; Pusporani, Elly; Salsabila, Fatiha Nadia; Nitasari, Alfi Nur; Lu’lu’a, Na’imatul
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87377

Abstract

This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features.  Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
Co-Authors Elly Pusporani M. Fariz Fadillah Mardianto Nitasari, Alfi Nur Salsabila, Fatiha Nadia