Araştırma Makalesi

A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines

Cilt: 28 Sayı: 82 27 Ocak 2026
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A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines

Öz

Brain tumors are among the most common causes of death in humans. Early and accurate detection of brain cancers is critical for effective treatment. Imaging techniques such as computed tomography, magnetic resonance imaging, X-rays, and ultrasound are used as a preliminary reference by illness experts. Different learning strategies have been employed in the field of health to diagnose diseases early, reduce the intensity of experts, and minimize diagnostic errors. Image processing studies in brain research have begun to provide successful findings in recent years, thanks to the developed of machine learning and deep learning models. In this study, as a novelty to the studies in the literature, a hybrid algorithm is proposed that features were extracted with pre-trained based CNN, classification was made with SVM based different kernels. As a result, the brain tumors were detected with 98% classification performance.

Anahtar Kelimeler

Proje Numarası

1059B141900679

Kaynakça

  1. Smith RA, Andrews KS, Brooks D, Fedewa SA, Manassaram-Baptiste D, Saslow D, et al. Cancer screening in the United States, 2017: A review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin 2017;67(2):100-21. doi:10.3322/caac.21392.
  2. Lu G, Fakurnejad S, Martin BA, van den Berg NS, van Keulen S, Nishio N, et al. Predicting Therapeutic Antibody Delivery into Human Head and Neck Cancers. Clin Cancer Res 2020;26(11):2582-94. doi:10.1158/1078-0432.
  3. WHO. World Health Organization Report. https://www.who.int/health-topics/cancer; 2022.
  4. Kidwell CS, Hsia AW. Imaging of the Brain and Cerebral Vasculature in Patients with Suspected Stroke: Advantages and Disadvantages of CT and MRI. Current Neurology and Neuroscience Reports 2006;6(1):9-16. doi:10.1007/s11910-996-0003-1.
  5. Vani N, Sowmya A, Jayamma N. Brain Tumor Classification using Support Vector Machine. International Research Journal of Engineering and Technology (IRJET) 2017;4(7):792-6.
  6. Mohsen HM, El-Dahshan EA, El-Horbaty EM, Salem AM. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal 2017;3(1):68-71. doi:10.1016/J.FCIJ.2017.12.001.
  7. Shahzadi I, Tang TB, Meriadeau F, Quyyum A. CNN-LSTM: Cascaded framework for brain tumour classification. IEEE EMBS Conference on Biomedical Engineering and Sciences 2018:633-7.
  8. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, et al. Brain Tumor Classification for MR Images using Transfer Learning and Fine-Tuning. Computerized Medical Imaging and Graphics 2019;75:34-46. doi:10.1016/j.compmedimag.2019.05.001.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Matematikte Optimizasyon, Uygulamalı Matematik (Diğer)

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

27 Ocak 2026

Gönderilme Tarihi

24 Ocak 2025

Kabul Tarihi

25 Kasım 2025

Yayımlandığı Sayı

Yıl 2026 Cilt: 28 Sayı: 82

Kaynak Göster

APA
Özer, E. (2026). A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 28(82), 157-162. https://doi.org/10.21205/deufmd.2026288220
AMA
1.Özer E. A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines. DEUFMD. 2026;28(82):157-162. doi:10.21205/deufmd.2026288220
Chicago
Özer, Ezgi. 2026. “A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 28 (82): 157-62. https://doi.org/10.21205/deufmd.2026288220.
EndNote
Özer E (01 Ocak 2026) A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 28 82 157–162.
IEEE
[1]E. Özer, “A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines”, DEUFMD, c. 28, sy 82, ss. 157–162, Oca. 2026, doi: 10.21205/deufmd.2026288220.
ISNAD
Özer, Ezgi. “A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 28/82 (01 Ocak 2026): 157-162. https://doi.org/10.21205/deufmd.2026288220.
JAMA
1.Özer E. A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines. DEUFMD. 2026;28:157–162.
MLA
Özer, Ezgi. “A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, c. 28, sy 82, Ocak 2026, ss. 157-62, doi:10.21205/deufmd.2026288220.
Vancouver
1.Ezgi Özer. A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines. DEUFMD. 01 Ocak 2026;28(82):157-62. doi:10.21205/deufmd.2026288220

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