The utilisation of image processing and deep learning techniques for the detection of brain tumours

Yıl 2025, Cilt: 30 Sayı: 3, 1009 - 1026, 24.12.2025

Sinem Balitatli , Fatmana Şentürk

https://doi.org/10.53433/yyufbed.1721560

Öz

The early diagnosis of brain tumours has been demonstrated to improve treatment options and patient survival rates. The utilisation of artificial intelligence in the detection of brain tumours has been demonstrated to enhance the efficacy of the process. The utilisation of deep learning methodologies for the detection of brain tumours through the analysis of Magnetic Resonance Imaging images has been demonstrated to be a highly efficacious approach for the identification of the disease in its early stages. Convolutional neural network is a highly sophisticated deep learning technique that has achieved considerable success in this field. The objective of this study is twofold: firstly, to enhance the performance of convolutional neural network models through the utilisation of image processing techniques; and secondly, to streamline the diagnostic phase. The Gauss filter, a denoising method, was employed to eliminate noise in images, while the Clahe method, a contrast enhancement technique, was utilised to address the contrast issue in images. In the proposed model, the classification made using the deep learning model had a validation accuracy of 96.48%, while when image processing methods were included, this value was calculated as 98.52%.

Anahtar Kelimeler

Brain tumor detection , Convolutional neural network , Deep learning , Image processing

Proje Numarası

1919B012321142

Görüntü İşleme ve Derin Öğrenme Teknikleri ile Beyin Tümör Tespiti

Öz

Beyin tümörlerinin erken teşhisi, tedavi seçeneklerini ve hastanın hayatta kalma oranlarını iyileştirir. Beyin tümörü tespitinde yapay zekâ kullanımı tespiti kolaylaştırmaktadır. Manyetik rezonans görüntüleme görüntüleri kullanılarak beyin tümörünü tespit etmek için Derin öğrenme yaklaşımları, hastalığın erken evresinde saptanmasında oldukça etkilidir. Bu alanda en başarılı derin öğrenme tekniklerinden evrişimli sinir ağı oldukça gelişmiş bir yöntemdir. Bu çalışmada, evrişimli sinir ağı model performansını görüntü işleme (image processing) teknikleri kullanarak artırmayı ve teşhiş aşamasını kolaylaştırmayı hedeflenmiştir. Gürültü giderme metotlarından gauss filtre görüntülerdeki gürültüyü gidermek ve kontrast iyileştirme metotlarından Clahe metodu görüntülerdeki kontrast sorunu iyileştirmek için kullanılmıştır. Önerilen modelde derin öğrenme modeli kullanılarak yapılan sınıflandırma %96.48 doğrulama başarı (validation accurracy) değerine sahipken, görüntü işleme metotları dahil edildiğinde bu değer %98.52 olarak hesaplanmıştır.

Anahtar Kelimeler

Beyin tümör tespiti , Derin öğrenme , Evrişimli sinir ağı , Görüntü işleme

Etik Beyan

Bu makalenin yazarları çalışmalarında araştırma ve yayın etiğine uyduklarını beyan ederler.

Destekleyen Kurum

TÜBİTAK

Proje Numarası

1919B012321142

Teşekkür

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 2209 – A projeleri kapsamında 1919B012321142 başvuru numarası ile desteklenmiştir. Projeye verdiği destekten ötürü TÜBİTAK’a teşekkürlerimizi sunarız.

Kaynakça

• Agarwal, M., Rani, G., Kumar, A., Kumar, P., Manikandan, R., & Gandomi, A. H. (2024). Deep learning for enhanced brain tumor detection and classification. Results in Engineering, 22, 102117. https://doi.org/10.1016/j.rineng.2024.102117
• Aiya, A. J., Wani, N., Ramani, M., Kumar, A., Pant, S., Kotecha, K., ... & Al-Danakh, A. (2025). Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability. Scientific Reports, 15(1), 31386. https://doi.org/10.1038/s41598-025-04591-3
• Akram, M. U., & Usman, A. (2011, July). Computer aided system for brain tumor detection and segmentation. In International conference on Computer networks and information technology (pp. 299-302). IEEE. https://doi.org/10.1109/ICCNIT.2011.6020885
• Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018). Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297. https://doi.org/10.1016/j.future.2018.04.065
• Anantharajan, S., Gunasekaran, S., & Subramanian, T. (2024). MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors, 31, 101026. https://doi.org/10.1016/j.measen.2024.101026 Anonim. (2025). Br35H: Brain Tumor Detection 2020. Erişim Tarihi: 30.05.2025. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection
• Arabahmadi, M., Farahbakhsh, R., & Rezazadeh, J. (2022). Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging. Sensors, 22(5), 1960. https://doi.org/10.3390/s22051960
• Archana, K. V., & Komarasamy, G. (2023). A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor. Journal of Intelligent Systems, 32(1), 20220206. https://doi.org/10.1515/jisys-2022-0206
• Ardan, I. S., & Indraswari, R. (2024, January). Design of brain tumor detection system on MRG image using CNN. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1388-1393). IEEE. https://doi.org/10.1109/ICETSIS61505.2024.10459651
• Asad, R., Rehman, S. U., Imran, A., Li, J., Almuhaimeed, A., & Alzahrani, A. (2023). Computer-aided early melanoma brain-tumor detection using deep-learning approach. Biomedicines, 11(1), 184. https://doi.org/10.3390/biomedicines11010184
• Asiri, A. A., Soomro, T. A., Shah, A. A., Pogrebna, G., Irfan, M., & Alqahtani, S. (2024). Optimized brain tumor detection: a dual-module approach for mri image enhancement and tumor classification. IEEE access, 12, 42868-42887. https://doi.org/10.1109/ACCESS.2024.3379136
• Aslan, E. (2024). LSTM-ESA hibrit modeli ile MR görüntülerinden beyin tümörünün sınıflandırılması. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 11(22), 63-81. https://doi.org/10.54365/adyumbd.1391157
• Aslan, E., & Özüpak, Y. (2025). Performance comparison of deep learning models in brain tumor classification. Balkan Journal of Electrical and Computer Engineering, 13(2), 203-209. https://doi.org/10.17694/bajece.1617698
• Bouhafra, S., & El Bahi, H. (2024). Deep learning approaches for brain tumor detection and classification using MRG images (2020 to 2024): A systematic review. Journal of Imaging Informatics in Medicine, 1-31. https://doi.org/10.1007/s10278-024-01283-8
• Bhardwaj, N. E. H. A., Sood, M., & Gill, S. (2024). Design of transfer learning-based deep CNN paradigm for brain tumor classification. WSEAS Trans. Biol. Biomed, 21, 162-169. https://doi.org/10.37394/23208.2024.21.17
• Divya Shree, C. K. (2022). Building efficient neural networks for brain tumor detection. Journal of Positive School Psychology, 6(11), 222-235.
• El-Dahshan, E. S. A., Mohsen, H. M., Revett, K., & Salem, A. B. M. (2014). Computer-aided diagnosis of human brain tumor through MRG: A survey and a new algorithm. Expert systems with Applications, 41(11), 5526-5545. https://doi.org/10.1016/j.eswa.2014.01.021
• Geetha, N., Rithika, V. A., Shobika, P., & Vyshnavi, S. (2023, April). Brain tumor detection using histogram equalization and deep learning. In 2023 IEEE International Conference on Contemporary Computing and Communications (InC4) (Vol. 1, pp. 1-6). IEEE. http://dx.doi.org/10.1109/InC457730.2023.10263104
• Guluwadi, S. (2024). Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50. BMC medical imaging, 24(1), 1-19. https://doi.org/10.1186/s12880-024-01292-7
• Islam, M. A., Noshin, S. A., Islam, M. R., Razy, M. F., Antara, S., Reza, M. T., & Parvez, M. Z. (2023, March). A low parametric cnn based solution to efficiently detect brain tumor cells from ultrasound scans. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)(pp.1152-1158). IEEE. https://doi.org/10.1109/CCWC57344.2023.10099302
• Islam, M. N., Azam, M. S., Islam, M. S., Kanchan, M. H., Parvez, A. S., & Islam, M. M. (2024). An improved deep learning-based hybrid model with ensemble techniques for brain tumor detection from MRI image. Informatics in Medicine Unlocked, 47, 101483. https://doi.org/10.1016/j.imu.2024.101483
• Jebin, B. M., Shyla, S. I., Bel, K. N. S., & Sheela, C. J. J. (2025). Brain tumor detection and classification using u-net and CNN with brain texture pattern analysis. Biomedical Signal Processing and Control, 110, 108156. https://doi.org/10.1016/j.bspc.2025.108156
• Kavitha, P., Dhinakaran, D., Prabaharan, G., & Manigandan, M. D. (2024). Brain Tumor Detection for Efficient Adaptation and Superior Diagnostic Precision by Utilizing MBConv-Finetuned-B0 and Advanced Deep Learning. International Journal of Intelligent Engineering & Systems, 17(2). https://doi.org/10.22266/ijies2024.0430.51
• Lakshmi, M. J., & Nagaraja Rao, S. (2022). RETRACTED ARTICLE: Brain tumor magnetic resonance image classification: a deep learning approach. Soft Computing, 26(13), 6245-6253. https://doi.org/10.1007/s00500-022-07163-z
• Logeswari, T., & Karnan, M. (2010). An improved implementation of brain tumor detection using segmentation based on soft computing. Journal of Cancer Research and Experimental Oncology, 2(1), 006-014. https://doi.org/10.5897/JCREO.9000002
• Modiya, P., & Vahora, S. (2022). Brain tumor detection using transfer learning with dimensionality reduction method. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 201-206. https://ijisae.org/index.php/IJISAE/article/view/1310/704
• Natha, S., Laila, U., Gashim, I. A., Mahboob, K., Saeed, M. N., & Noaman, K. M. (2024). Automated brain tumor identification in biomedical radiology images: A multi-model ensemble deep learning approach. Applied Sciences, 14(5), 2210. https://doi.org/10.3390/app14052210
• Nayan, A. A., Mozumder, A. N., Haque, M. R., Sifat, F. H., Mahmud, K. R., Azad, A. K. A., & Kibria, M. G. (2022). A deep learning approach for brain tumor detection using magnetic resonance imaging. arXiv preprint arXiv:2210.13882. https://doi.org/10.48550/arXiv.2210.13882
• Qin, P., Zhang, J., Zeng, J., Liu, H., & Cui, Y. (2019). A framework combining DNN and level-set method to segment brain tumor in multi-modalities MR image. Soft Computing, 23(19), 9237-9251. https://doi.org/10.1007/s00500-019-03778-x
• Patil, S., & Kirange, D. (2023). Ensemble of deep learning models for brain tumor detection. Procedia Computer Science, 218, 2468-2479. https://doi.org/10.1016/j.procs.2023.01.222
• Priya, A., & Vasudevan, V. (2024). Brain tumor classification and detection via hybrid alexnet-gru based on deep learning. Biomedical Signal Processing and Control, 89, 105716. https://doi.org/10.1016/j.bspc.2023.105716
• Rajendirane, R., Ananth Kumar, T., Sandhya, S. G., & Hu, Y. C. (2024). A novel brain tumor segmentation and classification model using deep neural network over MRG-flair images. Multimedia Tools and Applications, 1-32. https://doi.org/10.1007/s11042-024-19487-z
• Ramtekkar, P. K., Pandey, A., & Pawar, M. K. (2023). Innovative brain tumor detection using optimized deep learning techniques. International Journal of System Assurance Engineering and Management, 14(1), 459-473. https://doi.org/10.1007/s13198-022-01819-7
• Rasheed, Z., Ma, Y. K., Ullah, I., Ghadi, Y. Y., Khan, M. Z., Khan, M. A., ... & Shehata, A. M. (2023). Brain tumor classification from MRG using image enhancement and convolutional neural network techniques. Brain Sciences, 13(9), 1320. https://doi.org/10.3390/brainsci13091320
• Sahaai, M. B., Jothilakshmi, G. R., Prasath, R., & Singh, S. (2021). Brain tumor detection using DNN algorithm. Turkish Journal of Computer and Mathematics Education, 12(11), 3338-3345. https://www.proquest.com/scholarly-journals/brain-tumor-detection-using-dnn-algorithm/docview/2623922122/se-2
• Satyanarayana, G., Naidu, P. A., Desanamukula, V. S., & Rao, B. C. (2023). A mass correlation based deep learning approach using deep convolutional neural network to classify the brain tumor. Biomedical Signal Processing and Control, 81, 104395. https://doi.org/10.1016/j.bspc.2022.104395
• Schiavon, D. E. B., Becker, C. D. L., Botelho, V. R., & Pianoski, T. A. (2023, September). Interpreting convolutional neural networks for brain tumor classification: an explainable artificial intelligence approach. In Brazilian conference on intelligent systems (pp. 77-91). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45389-2_6
• Siar, M., & Teshnehlab, M. (2019, October). Brain tumor detection using deep neural network and machine learning algorithm. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 363-368). IEEE. https://doi.org/10.1109/ICCKE48569.2019.8964846
• Zubair Rahman, A. M. J., Gupta, M., Aarathi, S., Mahesh, T. R., Vinoth Kumar, V., Yogesh Kumaran, S., & Guluwadi, S. (2024). Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering. BMC Medical Informatics and Decision Making, 24(1), 113. https://doi.org/10.1186/s12911-024-02519-x