Performance Comparison of Deep Learning Models in Brain Tumor Classification

Yıl 2025, Cilt: 13 Sayı: 2 , 203 - 209 , 30.06.2025

Emrah Aslan , Yıldırım Özüpak

https://doi.org/10.17694/bajece.1617698

Cited By: 4

https://izlik.org/JA96PD86RY

Öz

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.

Anahtar Kelimeler

Brain Tumor , Classification , Deep Learning , YOLOv8 , Model Performance Comparison

Kaynakça

• [1] S. Hossain, A. Chakrabarty, T. R. Gadekallu, M. Alazab, and M. J. Piran, “Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification,” IEEE J Biomed Health Inform, vol. 28, no. 3, pp. 1261–1272, Mar. 2024, doi: 10.1109/JBHI.2023.3266614.
• [2] P. Kanchanamala, K. G. Revathi, and M. B. J. Ananth, “Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI,” Biomed Signal Process Control, vol. 84, p. 104955, Jul. 2023, doi: 10.1016/J.BSPC.2023.104955.
• [3] M. S. Mithun and S. Joseph Jawhar, “Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model,” J Radiat Res Appl Sci, vol. 17, no. 4, p. 101113, Dec. 2024, doi: 10.1016/J.JRRAS.2024.101113.
• [4] N. F. Alhussainan, B. Ben Youssef, and M. M. Ben Ismail, “A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7,” Computation 2024, Vol. 12, Page 44, vol. 12, no. 3, p. 44, Mar. 2024, doi: 10.3390/COMPUTATION12030044.
• [5] F. Tasnim, M. T. Islam, A. T. Maisha, I. Sultana, T. Akter, and M. T. Islam, “Comparison of Brain Tumor Detection Techniques by Using Different Machine Learning YOLO Algorithms,” Lecture Notes in Networks and Systems, vol. 869 LNNS, pp. 51–65, 2024, doi: 10.1007/978-981-99-9040-5_4.
• [6] M. F. Almufareh, M. Imran, A. Khan, M. Humayun, and M. Asim, “Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning,” IEEE Access, vol. 12, pp. 16189–16207, 2024, doi: 10.1109/ACCESS.2024.3359418.
• [7] S. Solanki, U. P. Singh, S. S. Chouhan, and S. Jain, “Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview,” IEEE Access, vol. 11, pp. 12870–12886, 2023, doi: 10.1109/ACCESS.2023.3242666.
• [8] N. Ullah, A. Javed, A. Alhazmi, S. M. Hasnain, A. Tahir, and R. Ashraf, “TumorDetNet: A unified deep learning model for brain tumor detection and classification,” PLoS One, vol. 18, no. 9, p. e0291200, Sep. 2023, doi: 10.1371/JOURNAL.PONE.0291200.
• [9] T. Rahman and M. S. Islam, “MRI brain tumor detection and classification using parallel deep convolutional neural networks,” Measurement: Sensors, vol. 26, p. 100694, Apr. 2023, doi: 10.1016/J.MEASEN.2023.100694.
• [10] [A. A. Asiri et al., “Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications,” Life 2023, Vol. 13, Page 1449, vol. 13, no. 7, p. 1449, Jun. 2023, doi: 10.3390/LIFE13071449.
• [11] B. V. Prakash et al., “Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform,” Scientific Reports 2023 13:1, vol. 13, no. 1, pp. 1–13, Sep. 2023, doi: 10.1038/s41598-023-41576-6.
• [12] M. A. Khan et al., “Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm,” Int J Imaging Syst Technol, vol. 33, no. 2, pp. 572–587, Mar. 2023, doi: 10.1002/IMA.22831.
• [13] M. Agarwal, G. Rani, A. Kumar, P. K. K, R. Manikandan, and A. H. Gandomi, “Deep learning for enhanced brain Tumor Detection and classification,” Results in Engineering, vol. 22, p. 102117, Jun. 2024, doi: 10.1016/J.RINENG.2024.102117.
• [14] K. Bhagyalaxmi, B. Dwarakanath, and P. V. P. Reddy, “Deep learning for multi-grade brain tumor detection and classification: a prospective survey,” Multimed Tools Appl, vol. 83, no. 25, pp. 65889–65911, Jul. 2024, doi: 10.1007/S11042-024-18129-8/TABLES/6.
• [15] O. Turk, D. Ozhan, E. Acar, T. C. Akinci, and M. Yilmaz, “Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images,” Zeitschrift für Medizinische Physik, vol. 34, no. 2, pp. 278–290, 2024, doi: 10.1016/j.zemedi.2022.11.010.
• [16] G. Vineela, G. H. Vardhan, C. Kesava Rao, T. Geetamma, and D. Drnivasa Rao, “Deep Learning Technique to detect Brain tumor disease using YOLO v8,” Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024, ICNWC 2024, 2024, doi: 10.1109/ICNWC60771.2024.10537552.
• [17] I. Pacal, O. Celik, B. Bayram, et al., “Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-based brain tumor classification,” Cluster Comput, vol. 27, pp. 11187–11212, 2024, doi: 10.1007/s10586-024-04532-1.
• [18] N. Elazab, W. A. Gab-Allah, and M. Elmogy, “A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–20, Feb. 2024, doi: 10.1038/s41598-024-54864-6.
• [19] “Brain Tumor Classification (MRI).” Accessed: Dec. 19, 2024. [Online]. Available: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri/data.
• [20] F. Alpsalaz and M. S. Mamiş, “Detection of Arc Faults in Transformer Windings via Transient Signal Analysis,” Appl. Sci., vol. 14, no. 20, p. 9335, 2024, doi: 10.3390/app14209335.
• [21] I. Pacal, “A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images,” Int. J. Mach. Learn. & Cyber., vol. 15, pp. 3579–3597, 2024, doi: 10.1007/s13042-024-02110-w.