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Journals / Firat University Journal of Experimental and Computational Engineering / 2026 / Cilt: 2 Sayı: 1

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

MR görüntüleri kullanılarak öznitelik seçimine dayalı ön-eğitilmiş bir derin öğrenme modeliyle beyin tümörünün tespiti

Kürşat Demir(FIRAT ÜNİVERSİTESİ)0000-0003-3408-3505
Berna Arı(FIRAT ÜNİVERSİTESİ)0000-0003-1000-2619
Fatih Demir(FIRAT ÜNİVERSİTESİ)0000-0003-3210-3664
Pages
23–31
Publication Date
2023-02-13
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the most dangerous diseases in the world is a brain tumor.
📊
Key Findings & Data:A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull.
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Conclusion & Impact:As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Öznitelik seçimi · ReliefF algoritması · Beyin tümörü

Abstract

One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to death if not diagnosed early. Magnetic Resonance Imaging (MRI) is a diagnostic method that is frequently used in soft tissues and gives successful results. In this study, a brain tumor was automatically detected from MR images. For feature extraction, a pre-trained Convolutional Neural Network (CNN) model named MobilenetV2 was used. Then, the ReliefF algorithm was used for feature selection. The features extracted with MobileNetV2 and the features selected with the ReliefF algorithm are given separately to the classifiers and the system performance is tested. As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.

Özet

Dünyadaki en tehlikeli hastalıklardan biri beyin tümörüdür. Bir beyin tümörü beyindeki sağlıklı dokuyu yok eder ve daha sonra anormal şekilde çoğalarak kafatasında iç basıncın artmasına neden olur. Bu erken teşhis edilmezse ölüme yol açabilir. Manyetik Rezonans Görüntüleme (MRG) yumuşak dokularda sıklıkla kullanılan ve başarılı sonuçlar veren bir tanı yöntemidir. Bu çalışmada, MR görüntülerinden bir beyin tümörü otomatik olarak tespit edildi. Öznitelik çıkarımı için MobilenetV2 adlı önceden eğitilmiş bir Evrişimsel Sinir Ağı modeli kullanılmıştır. Daha sonra öznitelik seçimi için ReliefF algoritması kullanılmıştır. MobileNetV2 ile çıkarılan öznitelikler ve ReliefF algoritması ile seçilen öznitelikler ayrı ayrı sınıflandırıcılara verilerek sistem performansı test edilmiştir. Deneysel çalışmalar sonucu MobileNetV2 öznitelik çıkarımı, ReliefF algoritması öznitelik seçimi ve KNN sınıflandırıcı kombinasyonuyla en yüksek başarımın elde edildiği görülmüştür.

🔖

Cite This Article

Demir, K., Arı, B., Demir, F. (2023). Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images. Firat University Journal of Experimental and Computational Engineering, Cilt: 2 Sayı: 1, 23–31. https://doi.org/10.5505/fujece.2023.36844

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References (48)

  1. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H. “Brain tumor segmentation with Deep Neural Networks”. Medical Image Analysis., 35, 18–31, 2017.
  2. American Society of Clinical Oncology. https://www.cancer.net/cancer-types/brain-tumor/statistics
  3. Petruzzi A, Finocchiaro CY, Lamperti E, Salmaggi A. “Living with a brain tumor”, Supportive. Care in Cancer. 21(4), 1105–1111, 2013.
  4. Mohammed M, Nalluru SS, Tadi S, Samineni R. “Brain tumor image classification using convolutional neural networks”. Int. J. Adv. Sci. Technol. 29(5), 928–934, 2019.
  5. Ucuz I, Ari A, Ozcan OO, Topaktas O, Sarraf M, Dogan O. “Estimation of the development of depression and PTSD in children exposed to sexual abuse and development of decision support systems by using artificial intelligence”. Journal of child sexual abuse, 31(1), 73-85, 2022.
  6. Tasci I, Tasci B, Doğan S, Tuncer T. “A new dataset for EEG abnormality detection MTOUH”. Turkish Journal of Science and Technology, 17(1), 135-141, 2022.
  7. Toğaçar M, Cömert Z, Ergen B. “Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks”. Chaos, Solitons and Fractals, 144, 110714, 2021.
  8. Demir F, Tasci B. “An Effective and Robust Approach Based on R-CNN+ LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images”. Journal of Personalized Medicine, 11(12), 1276, 2021.
  9. Tasci B. “Ön Eğitimli Evrişimsel Sinir Ağı Modellerinde Öznitelik Seçim Algoritmasını Kullanarak Cilt Lezyon Görüntülerinin Sınıflandırılması”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 541-552, 2022.
  10. Loh HW, Ooi CP, Aydemir E, Tuncer T, Dogan S, Acharya UR. “ Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals” Expert Systems, 39(3), e12773, 2022.
  11. Tasci B, Tasci G, Dogan S,Tuncer T. “A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals”. Cognitive Neurodynamics, 1-14, 2022.
  12. Demir F, Akbulut Y, Taşcı B, Demir K. “Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data”. Biomedical Signal Processing and Control, 81, 104424, 2023.
  13. Tasci G, Loh W, Barua D, Baygin M, Tasci B, Dogan S, Acharya, UR. “Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals”. Knowledge-Based Systems, 260, 110190, 2023.
  14. Dogan S, Baygin M, Tasci B, Loh HW, Barua PD, Tuncer T, Acharya UR. “Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals”. Cognitive Neurodynamics, 1-13, 2022.
  15. Tasci B. “Beyin MR görüntülerinden mrmr tabanlı beyin tümörlerinin sınıflandırması”. Journal of Scientific Reports-B, 6, 1-9, 2022.
  16. Tasci B. “A Classification Method for Brain MRI via AlexNet”. International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), IEEE, 347-35, 2021.
  17. Karadal CH, Kaya MC, Tuncer T, Dogan S, Acharya UR. “Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques., Expert Systems with Applications, 185, 115659, 2021.
  18. Demir F. “DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images”, Applied Soft Computing, 103, 107160, 2021.
  19. Demir F. “DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images”. Biocybernetics and Biomedical Engineering, 41(3), 1123–1139, 2021.
  20. Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR. “Convolutional neural networks for multi-class brain disease detection using MRI images”. Computerized Medical Imaging and Graphics, 78, 101673 2019.
  21. Lu SY, Wang SH, Zhang YD. “A classification method for brain MRI via MobileNet and feedforward network with random weights”. Pattern Recognit. Lett. 140, 252–260, 2020.
  22. Talo M, Baloglu UB, Yıldırım Ö, Acharya UR. “Application of deep transfer learning for automated brain abnormality classification using MR images”. Cognitive Systems Research, 54, 176–188, 2019.
  23. Kumar S, Mankame DP. “Optimization driven Deep Convolution Neural Network for brain tumor classification”. Biocybern. Biomed. Eng., 40(3), 1190–1204, 2020.
  24. Raja PMS. “Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach”. Biocybern. Biomed. Eng., 40(1), 440–453, 2020.
  25. Devi UK, Gomathi R. “Brain tumour classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN)”. Journal of Ambient Intelligence Humanized Computing, 12(6), 6263–6273, 2021.
  26. Alhassan AM, Zainon WMNW. “Brain tumor classification in magnetic resonance image using hard swish-based RELU activation function-convolutional neural network”. Neural Computing and Applications, 33(15), 9075–9087, 2021.
  27. Kumar RL, Kakarla J, Isunuri BV, Singh M. “Multi-class brain tumor classification using residual network and global average pooling”. Multimedia Tools and Applicaitons, 80(19), 13429–13438, 2021.
  28. Kokkalla S, Kakarla J, Venkateswarlu IB, Singh M. “Three-class brain tumor classification using deep dense inception residual network”. Soft Computing, 25(13), 8721–8729, 2021.
  29. Toğaçar M, Cömert Z, Ergen B. “Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method”. Expert Systems with Applications, 149, 113274, 2020.
  30. Kang J, Ullah Z, Gwak J. “Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers”. Sensors, 21(6) 1–21, 2021.
  31. Arı A, Alcin OF, Hanbay D. “Brain MR Image Classification Based on Deep Features by Using Extreme Learning Machines”. Biomedical Journal of Scientific and Technical Research, 25(3), 2020.
  32. Alcin ÖF, Korkmaz D, Ekici S, Şengür A. “An Artificial Neural Network Model for The Amperes Law”. Global Journal on Technology, 4(2), 2013.
  33. Turkoglu M, Aslan M, Arı A, Alçin ZM, Hanbay D. “A multi-division convolutional neural network-based plant identification system”. PeerJ Computer Science, 7, e572, 2021.
  34. Arı A. “Analysis of EEG signal for seizure detection based on WPT”. Electronics Letters, 56(25), 1381-1383, 2020.
  35. Ari B, Ucuz I, Ari A, Ozdemir F, Sengur A. “Grafik Tablet Kullanılarak Makine Öğrenmesi Yardımı ile El Yazısından Cinsiyet Tespiti”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 243-252, 2020.
  36. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of the IEEE conference on computer vision and pattern recognition, 4510-4520, Jan 2018.
  37. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”. arXiv, 1704.04861, 2017.
  38. Karadal CH, Kaya M, Tuncer T, Dogan, S, Acharya UR. “Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques”. Expert Systems with Applications, 185, 115659, 2021.
  39. Kira, K, Rendell LA. “The feature selection problem: Traditional methods and new algorithm”. Proceedings of AAAI’92, 2, 129-134, 1992.
  40. Kira K, Rendell LA. “A practical approach to feature selection”. Machine Learning: Proceedings of International Conference (ICML’92), 249–256, 1992.
  41. Tasci B, Tasci I. “Deep feature extraction based brain image classification model using preprocessed images: PDRNet”. Biomedical Signal Processing and Control, 78, 103948, 2022.
  42. Macin G, Tasci B, Tasci I, Faust O, Barua PD, Dogan S, Acharya UR. “An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ”. Applied Sciences, 12(10), 4920, 2022
  43. Demir K, Ay M, Cavas M, Demir F. “Automated steel surface defect detection and classification using a new deep learning-based approach”. Neural Computing and Applications, 1-18, 2022.
  44. Chakrabarty N. “Brain MRI images for brain tumor detection”. https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection/metadata
  45. Nanda A, Barik RC, Bakshi S. “SSO-RBNN driven brain tumor classification with Saliency-K-means segmentation technique”. Biomedical Signal Processing and Control, 81, 104356, 2023.
  46. Demir F, Akbulut Y. “A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification. Biomedical Signal Processing and Control”. 75, 103625, 2022.
  47. Alnabhan M, Habboush AK, Abu QA, Mohanty AK, Pattnaik S, Pattanayak BK. “Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification”. Mobile Information Systems, 2022, 2022.
  48. Asif S, Yi W, Ain QU, Hou J, Yi T, Si J. “Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images”. IEEE Access, 10, 34716-34730, 2022.