Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

MUSA YILMAZ

10.17694/bajece.1617698

Computer Software

Bilgisayar Yazılımı

Performance Comparison of Deep Learning Models in Brain Tumor Classification

https://orcid.org/0000-0002-0181-3658

Aslan

Emrah

Mardin Artuklu

https://orcid.org/0000-0001-8461-8702

Özüpak

Yıldırım

DICLE UNIVERSITY

06 30 2025

13 2 203 209 01 11 2025 03 11 2025

2013

Balkan Journal of Electrical and Computer Engineering

Accurate and timely detection of brain tumors is critical for successful treatment. Magnetic Resonance Imaging (MRI) is an essential tool that provides invaluable information for the recognition of different types of brain tumors such as glioma, meningioma, pituitary tumors and benign entities. However, distinguishing between these tumor types and taking preventive measures poses a significant challenge in the classification of brain tumors. Compared to traditional disease detection methods, artificial intelligence-based computer applications offer significant contributions to brain tumor detection. In particular, deep learning methods, which have gained popularity in disease detection through the analysis of medical images, play a critical role in this process. Several deep learning techniques have been reported in the literature for brain tumor classification. In this study, the YOLOv8s-cls model is used to detect brain tumors from MRI scans. The proposed model showed a high success rate of 98.7% accuracy during the experimental studies. The results show that the YOLOv8 model not only outperforms existing methods but also proves to be an effective approach for image classification.

Brain Tumor Classification Deep Learning YOLOv8 Model Performance Comparison

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