Keywords
Deep learning
Image processing
Magnetic resonance imaging (MRI)
Convolutional neural network (CNN)
How to Cite
Abstract
Brain tumor occurs when cells formed as a result of self-renewal of cells in the human body grow more than normal and become a mass. Brain tumor constitutes one of the factors that endanger human life. By early diagnosis with the right methods and techniques, lives can be saved by preventing brain tumors that endanger human life. In today's technology, Magnetic Resonance imaging (MRI) is used to detect brain tumors. Early diagnosis plays an important role in brain tumor. In this study, Convolution neural network (CNN) is used for brain tumor detection and classification with deep learning, a sub-branch of machine learning. When the CNN model was compared with other deep learning models for brain tumor prediction, it was found that the CNN model had a higher accuracy rate than other models, with 98.24%.
References
Altun, S. and A. Alkan, 2019. MR Spektroskopi Temelli Beyin Tümörü Teşhisinde Lojistik Regresyon Uygulaması. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 22: 10–18.
Ari, A. and D. Hanbay, 2018. Deep learning based brain tumor classification and detection system. Turkish Journal of Electrical Engineering and Computer Sciences, 26: 2275–2286.
Arı, A. and D. Hanbay, 2019. Bölgesel Evrişimsel Sinir Ağları Tabanlı MR Görüntülerinde Tümör. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34: 1395–1408.
Aslan, M., 2022. Derin Öğrenme Tabanlı Otomatik Beyin Tümör Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34: 399–407.
Bingöl, H. and B. Alataş, 2021. Classification of brain tumor images using deep learning methods. Turkish Journal of Science and Technology, 16: 137–143.
Bulut, F., Kılıç and F. İnce, 2018. Beyin Tümörü Tespitinde Görüntü Bölütleme Yöntemlerine Ait Başarımların Karşılaştırılması ve Analizi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 20: 173–186.
Karakurt, M. and İ. İşeri, 2022. Patoloji görüntülerinin derin öğrenme yöntemleri ile sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, pp. 192–206.
Menze, B. H., A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, et al., 2014. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 34: 1993–2024.
Metlek, S. and K. Kayaalp, 2020. Derin öğrenme ve destek vektör makineleri ile görüntüden cinsiyet tahmini. Düzce University Journal of Science and Technology, 8: 2208–2228.
Mohsen, H., E.-S. El-Dahshan, E.-S. El-Horbarty, and A.-B. M. Salem, 2017. Classification using Deep Learning Neural Networks for Brain Tumors. Future Computing and Informatics Journal, 3.
Qasem, S. N., A. Nazar, A. Qamar, S. Shamshirband, and A. Karim, 2019. A Learning Based Brain Tumor Detection System. Computers, Materials & Continua, 59.
Shahzadi, I., T. B. Tang, F. Meriadeau, and A. Quyyum, 2018. CNN-LSTM: Cascaded framework for brain tumour classification. In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 633–637, IEEE.
Taş, M. O. and S. Ergin, 2020. Detection of the Brain Tumor Existence Using a Traditional Deep Learning Technique and Determination of Exact Tumor Locations Using K-Means Segmentation in MR Images. İleri Mühendislik Çalışmaları ve Teknolojileri Dergisi, 1: 91–97.
Vani, N., A. Sowmya, and N. Jayamma, 2017. Brain tumor classification using support vector machine. International Research Journal of Engineering and Technology (IRJET), 4: 792–796.
Şükrü Çağlar, 2025. Beyin Tümörleri. https://www.sukrucaglar.com/tr/icerik/20/beyin-tumorleri
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.