KaydedilenlerDerginizi ekleyin

Dergiler / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 41 Sayı: 1

Aktivasyon fonksiyonları ve derin öğrenme mimarilerinin beyin MRI görüntüleri üzerindeki sınıflandırma performanslarının incelenmesi

Yasin Özkan(ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ)0000-0002-2029-0856
Sayfa
519–532
Yayın Tarihi
2026-03-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu bulgular, önerilen yöntemin sınıflandırma başarısını önemli ölçüde artırdığını ortaya koymaktadır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Beyin tümörü tespiti, tıbbi görüntüleme alanında en kritik süreçlerden biri olup, manyetik rezonans görüntüleme (MRI) yüksek çözünürlüklü yumuşak doku detayları sayesinde tanı sürecinde önemli bir rol oynamaktadır.
📊
Temel Bulgular & Veri:Ancak MRI görüntülerinin manuel olarak değerlendirilmesi zaman alıcı ve hataya açık bir süreçtir.
💡
Sonuç & Katkı:Bu bulgular, önerilen yöntemin sınıflandırma başarısını önemli ölçüde artırdığını ortaya koymaktadır.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Beyin tümörü sınıflandırması · MRI görüntü · hibrit aktivasyon fonksiyonları

Özet

Beyin tümörü tespiti, tıbbi görüntüleme alanında en kritik süreçlerden biri olup, manyetik rezonans görüntüleme (MRI) yüksek çözünürlüklü yumuşak doku detayları sayesinde tanı sürecinde önemli bir rol oynamaktadır. Ancak MRI görüntülerinin manuel olarak değerlendirilmesi zaman alıcı ve hataya açık bir süreçtir. Bu nedenle, derin öğrenme tabanlı otomatik sistemlerin geliştirilmesi, tanı hızını artırmak ve sağlık hizmetlerinin etkinliğini yükseltmek açısından büyük önem taşımaktadır. Bu çalışmada, Brain MRI görüntülerinden oluşan bir veri seti kullanılarak beyin tümörlerinin sınıflandırılması amaçlanmıştır. Öncelikle, veri setine görüntü işleme teknikleri uygulanarak iyileştirilmiş görüntüler elde edilmiştir. Ardından, orijinal ve iyileştirilmiş görüntüler yedi farklı transfer öğrenme mimarisine girdi olarak verilmiştir. Sonuçlar, tüm modellerde iyileştirilmiş görüntülerin daha yüksek doğruluk sağladığını göstermiştir. Çalışmanın ikinci aşamasında, en başarılı iki model olan ResNet101 ve ResNet152 üzerinde farklı aktivasyon fonksiyonları test edilmiştir. Önerilen hibrit aktivasyon fonksiyonu ile ResNet101 %98,42, ResNet152 ise %97,20 doğruluk oranına ulaşarak en yüksek performansı göstermiştir. Bu bulgular, önerilen yöntemin sınıflandırma başarısını önemli ölçüde artırdığını ortaya koymaktadır.

🔖

Bu Makaleye Atıf Yap

Özkan, Y. (2026). Aktivasyon fonksiyonları ve derin öğrenme mimarilerinin beyin MRI görüntüleri üzerindeki sınıflandırma performanslarının incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 41 Sayı: 1, 519–532. https://doi.org/10.17341/gazimmfd.1631531

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (83)

  1. Sitburana O., Ondo W.G., Brain Magnetic Resonance Imaging (MRI) in Parkinsonian Disorders, Parkinsonism & Related Disorders, 15(3), 165–174, 2009.
  2. Olanow C.W., Hauser R.A., Magnetic Resonance Imaging in Neurodegenerative Diseases, Neurodegenerative Diseases, 445–469, 1994.
  3. Litjens G., Kooi T., Bejnordi B.E., Setio A.A.A., Ciompi F., Ghafoorian M., Sánchez C.I., A Survey on Deep Learning in Medical Image Analysis, Medical Image Analysis, 42, 60–88, 2017.
  4. LeCun Y., Bengio Y., Hinton G., Deep Learning, Nature, 521(7553), 436–444, 2015.
  5. Patel S., Bharath K.P., Balaji S., Muthu R.K., Comparative Study on Histogram Equalization Techniques for Medical Image Enhancement, Soft Computing for Problem Solving, 1, 657–669, 2018.
  6. Jähne B., Digital Image Processing, Springer Science & Business Media, Berlin, Germany, 2005.
  7. Rajinikanth V., Priya E., Lin H., Lin F., Hybrid Image Processing Methods for Medical Image Examination, CRC Press, Boca Raton, USA, 2021.
  8. LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11), 2278–2324, 1998.
  9. Kamnitsas K., Ledig C., Newcombe V.F., Simpson J.P., Kane A.D., Menon D.K., Glocker B., Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation, Medical Image Analysis, 36, 61–78, 2017.
  10. Peng S., Chen W., Sun J., Liu B., Multi-Scale 3D U-Nets: An Approach to Automatic Segmentation of Brain Tumor, International Journal of Imaging Systems and Technology, 30(1), 5–17, 2020.
  11. Nazir M., Wahid F., Ali Khan S., A Simple and Intelligent Approach for Brain MRI Classification, Journal of Intelligent & Fuzzy Systems, 28(3), 1127–1135, 2015.
  12. Korolev S., Safiullin A., Belyaev M., Dodonova Y., Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification, IEEE ISBI, Washington DC, USA, 835–838, April 2017.
  13. Zhang Y., Dong Z., Wu L., Wang S., A Hybrid Method for MRI Brain Image Classification, Expert Systems with Applications, 38(8), 10049–10053, 2011.
  14. Assam M., Kanwal H., Farooq U., Shah S.K., Mehmood A., Choi G.S., An Efficient Classification of MRI Brain Images, IEEE Access, 9, 33313–33322, 2021.
  15. Fayaz M., Torokeldiev N., Turdumamatov S., Qureshi M.S., Qureshi M.B., Gwak J., An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network, Sensors, 21(22), 7480, 2021.
  16. Badža M.M., Barjaktarović M.Č., Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network, Applied Sciences, 10(6), 1999, 2020.
  17. Abiwinanda N., Hanif M., Hesaputra S.T., Handayani A., Mengko T.R., Brain Tumor Classification Using Convolutional Neural Network, World Congress on Medical Physics and Biomedical Engineering, 183–189, 2019.
  18. Vankdothu R., Hameed M.A., Brain Tumor MRI Images Identification and Classification Based on the Recurrent Convolutional Neural Network, Measurement: Sensors, 24, 100412, 2022.
  19. Chelghoum R., Ikhlef A., Hameurlaine A., Jacquir S., Transfer Learning Using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images, IFIP International Conference on Artificial Intelligence Applications and Innovations, 189–200, 2020.
  20. Çinar A., Yildirim M., Detection of Tumors on Brain MRI Images Using the Hybrid Convolutional Neural Network Architecture, Medical Hypotheses, 139, 109684, 2020.
  21. Yazdan S.A., Ahmad R., Iqbal N., Rizwan A., Khan A.N., Kim D.H., An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD, Tomography, 8(4), 1905–1927, 2022.
  22. 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.
  23. Jibon F.A., Khandaker M.U., Miraz M.H., Thakur H., Rabby F., Tamam N., Osman H., Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation, Healthcare, 10(9), 1801, 2022.
  24. Abd El Kader I., Xu G., Shuai Z., Saminu S., Javaid I., Salim Ahmad I., Differential Deep Convolutional Neural Network Model for Brain Tumor Classification, Brain Sciences, 11(3), 352, 2021.
  25. Mzoughi H., Njeh I., Wali A., Slima M.B., BenHamida A., Mhiri C., Mahfoudhe K.B., Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification, Journal of Digital Imaging, 33, 903–915, 2020.
  26. Musallam A.S., Sherif A.S., Hussein M.K., A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images, IEEE Access, 10, 2775–2782, 2022.
  27. Remzan N., Tahiry K., Farchi A., Brain Tumor Classification in Magnetic Resonance Imaging Images Using Convolutional Neural Network, International Journal of Electrical and Computer Engineering, 12(6), 6664–6672, 2022.
  28. Talo M., Yildirim O., Baloglu U.B., Aydin G., Acharya U.R., Convolutional Neural Networks for Multi-Class Brain Disease Detection Using MRI Images, Computerized Medical Imaging and Graphics, 78, 101673, 2019.
  29. Rahman T., Islam M.S., MRI Brain Tumor Detection and Classification Using Parallel Deep Convolutional Neural Networks, Measurement: Sensors, 26, 100694, 2023.
  30. Choudhuri R., Halder A., Brain MRI Tumour Classification Using Quantum Classical Convolutional Neural Net Architecture, Neural Computing and Applications, 35(6), 4467–4478, 2023.
  31. Hazarika R.A., Maji A.K., Kandar D., Jasinska E., Krejci P., Leonowicz Z., Jasinski M., An Approach for Classification of Alzheimer’s Disease Using Deep Neural Network and Brain Magnetic Resonance Imaging (MRI), Electronics, 12(3), 676, 2023.
  32. Rahman T., Islam M.S., MRI Brain Tumor Classification Using Deep Convolutional Neural Network, International Conference on Innovations in Science, Engineering and Technology, 451–456, 2022.
  33. Choudhury C.L., Mahanty C., Kumar R., Mishra B.K., Brain Tumor Detection and Classification Using Convolutional Neural Network and Deep Neural Network, International Conference on Computer Science, Engineering and Applications, 1–4, 2020.
  34. Shen D., Wu G., Suk H.I., Deep Learning in Medical Image Analysis, Annual Review of Biomedical Engineering, 19(1), 221–248, 2017.
  35. Razzak M.I., Naz S., Zaib A., Deep Learning for Medical Image Processing: Overview, Challenges and the Future, Classification in BioApps: Automation of Decision Making, 323–350, 2018.
  36. Nickparvar M., Brain Tumor MRI Dataset, Kaggle, https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset, Erişim Tarihi: 25.02.2026.
  37. Esteva A., Robicquet A., Ramsundar B., Kuleshov V., DePristo M., Chou K., Dean J., A Guide to Deep Learning in Healthcare, Nature Medicine, 25(1), 24–29, 2019.
  38. Akkus Z., Galimzianova A., Hoogi A., Rubin D.L., Erickson B.J., Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions, Journal of Digital Imaging, 30, 449–459, 2017.
  39. Shaheen F., Verma B., Asafuddoula M., Impact of Automatic Feature Extraction in Deep Learning Architecture, International Conference on Digital Image Computing: Techniques and Applications, 1–8, 2016.
  40. Wang S., Peng Y., Lu L., Lu Z., Bagheri M., Summers R.M., Deep Learning in Medical Image Analysis: A Review, Current Imaging and Labeling Systems, 14, 1449–1474, 2019.
  41. Yu X., Wang J., Hong Q.Q., Teku R., Wang S.H., Zhang Y.D., Transfer Learning for Medical Images Analyses: A Survey, Neurocomputing, 489, 230–254, 2022.
  42. Krizhevsky A., Sutskever I., Hinton G.E., Imagenet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 25, 1–9, 2012.
  43. Gonzales R.C., Wintz P., Digital Image Processing, Addison-Wesley Longman Publishing, Boston, USA, 1987.
  44. Vrbančič G., Podgorelec V., Transfer Learning with Adaptive Fine-Tuning, IEEE Access, 8, 196197–196211, 2020.
  45. Pan S.J., Yang Q., A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359, 2009.
  46. Glorot X., Bengio Y., Understanding the Difficulty of Training Deep Feedforward Neural Networks, International Conference on Artificial Intelligence and Statistics (AISTATS), 249–256, 2010.
  47. Hinton G.E., Deng L., Yu D., Dahl G.E., Mohamed A.R., Deep Neural Networks for Acoustic Modeling in Speech Recognition, IEEE Signal Processing Magazine, 29(6), 82–97, 2012.
  48. He K., Zhang X., Ren S., Sun J., Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852, 2015.
  49. LeCun Y., Bottou L., Orr G.B., Müller K.R., Efficient BackProp, Neural Networks: Tricks of the Trade, 9–48, 2012.
  50. Bishop C.M., Pattern Recognition and Machine Learning, Springer, New York, USA, 2006.
  51. Mastromichalakis S., Parametric Leaky Tanh: A New Hybrid Activation Function for Deep Learning, arXiv:2310.07720, 2023.
  52. Nair V., Hinton G.E., Rectified Linear Units Improve Restricted Boltzmann Machines, International Conference on Machine Learning, 807–814, 2010.
  53. Goodfellow I., Bengio Y., Courville A., Deep Learning, MIT Press, Cambridge, USA, 2016.
  54. Bengio Y., Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, 2(1), 1–127, 2009.
  55. Esteva A., Kuprel B., Novoa R.A., Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks, Nature, 542(7639), 115–118, 2017.
  56. Jaiswal A.K., Tiwari P., Kumar S., Gupta D., Khanna A., Rodrigues J.J., Identifying Pneumonia in Chest X-Rays: A Deep Learning Approach, Measurement, 145, 511–518, 2019.
  57. Dubey S.R., Singh S.K., Chaudhuri B.B., Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark, Neurocomputing, 503, 92–108, 2022.
  58. Hao W., Yizhou W., Yaqin L., Zhili S., The Role of Activation Function in CNN, International Conference on Information Technology and Computer Application, 429–432, 2020.
  59. Yadhav S.Y., Senthilkumar T., Jayanthy S., Kovilpillai J.J.A., Plant Disease Detection and Classification Using CNN Model with Optimized Activation Function, International Conference on Electronics and Sustainable Communication Systems, 564–569, 2020.
  60. Kiliçarslan S., Celik M., RSigELU: A Nonlinear Activation Function for Deep Neural Networks, Expert Systems with Applications, 174, 114805, 2021.
  61. Huang Z., Du X., Chen L., Li Y., Liu M., Chou Y., Jin L., Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification with a Modified Activation Function, IEEE Access, 8, 89281–89290, 2020.
  62. Wang J., Zhu H., Wang S.H., Zhang Y.D., A Review of Deep Learning on Medical Image Analysis, Mobile Networks and Applications, 26(1), 351–380, 2021.
  63. Shinde V., Deep Learning Approaches for Medical Image Analysis and Disease Diagnosis, International Journal of Multidisciplinary Innovation and Research Methodology, 2(2), 57–66, 2023.
  64. Hu M., Zhong Y., Xie S., Lv H., Lv Z., Fuzzy System Based Medical Image Processing for Brain Disease Prediction, Frontiers in Neuroscience, 15, 714318, 2021.
  65. Bhattacharya S., Maddikunta P.K.R., Pham Q.V., Gadekallu T.R., Chowdhary C.L., Alazab M., Piran M.J., Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey, Sustainable Cities and Society, 65, 102589, 2021.
  66. Suganyadevi S., Seethalakshmi V., Balasamy K., A Review on Deep Learning in Medical Image Analysis, International Journal of Multimedia Information Retrieval, 11(1), 19–38, 2022.
  67. Chowdhary C.L., Acharjya D.P., Segmentation and Feature Extraction in Medical Imaging: A Systematic Review, Procedia Computer Science, 167, 26–36, 2020.
  68. Atik İ.İ., Pneumonia detection on chest x-ray images using residual convolutional neural network, Journal of the Faculty of Engineering and Architecture of Gazi University, 39(3), 1719–1731, 2024.
  69. Güler H., Avcı D., Ulaş M., Omma T., In-depth analysis of machine learning models and explainable artificial intelligence methods in diabetes diagnosis, Journal of the Faculty of Engineering and Architecture of Gazi University, 40(3), 1995–2012, 2025.
  70. Kesav N., Jibukumar M.G., Efficient and Low Complex Architecture for Detection and Classification of Brain Tumor Using RCNN with Two Channel CNN, Journal of King Saud University-Computer and Information Sciences, 34(8), 6229–6242, 2022.
  71. Amin J., Sharif M., Haldorai A., Yasmin M., Nayak R.S., Brain Tumor Detection and Classification Using Machine Learning: A Comprehensive Survey, Complex & Intelligent Systems, 8(4), 3161–3183, 2022.
  72. Jia Z., Chen D., Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques, IEEE Access, 8, 1–12, 2020.
  73. Khairandish M.O., Sharma M., Jain V., Chatterjee J.M., Jhanjhi N.Z., A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images, IRBM, 43 (4), 290–299, 2022.
  74. Arif M., Ajesh F., Shamsudheen S., Geman O., Izdrui D., Vicoveanu D., Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques (Retracted), Journal of Healthcare Engineering, 2022, 2693621, 2022.
  75. Kandimalla S.Y., Vamsi D.M., Bhavani S., VM M., Recent Methods and Challenges in Brain Tumor Detection Using Medical Image Processing, Recent Patents on Engineering, 17 (5), 8–23, 2023.
  76. Tazin T., Sarker S., Gupta P., Ayaz F.I., Islam S., Monirujjaman Khan M., Alshazly H., A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network (Retracted), Computational Intelligence and Neuroscience, 2021, 2392395, 2021.
  77. Hossain T., Shishir F.S., Ashraf M., Al Nasim M.A., Shah F.M., Brain Tumor Detection Using Convolutional Neural Network, International Conference on Advances in Science, Engineering and Robotics Technology, 1–6, 2019.
  78. Seetha J., Raja S.S., Brain Tumor Classification Using Convolutional Neural Networks, Biomedical & Pharmacology Journal, 11 (3), 1457–1464, 2018.
  79. Kalkan M., Guzel M.S., Ekinci F., Akcapinar Sezer E., Asuroglu T., Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification, Cancers, 16 (19), 3321, 2024.
  80. Emek Soylu B., Guzel M.S., Bostanci G.E., Ekinci F., Asuroglu T., Acici K., Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review, Electronics, 12 (12), 2730, 2023.
  81. Atilkan Y., Kirik B., Acici K., Benzer R., Ekinci F., Guzel M.S., Asuroglu T., Advancing Crayfish Disease Detection: A Comparative Study of Deep Learning and Canonical Machine Learning Techniques, Applied Sciences, 14 (14), 6211, 2024.
  82. Ozsari S., Kumru E., Ekinci F., Akata I., Guzel M.S., Acici K., Asuroglu T., Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification, Sensors, 24 (22), 7189, 2024.
  83. Ekinci F., Ugurlu G., Ozcan G.S., Acici K., Asuroglu T., Kumru E., Akata I., Classification of Mycena and Marasmius Species Using Deep Learning Models: An Ecological and Taxonomic Approach, Sensors, 25 (6), 1642, 2025.