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MANYETİK REZONANS GÖRÜNTÜLERİNDE OTOMATIK BEYİN TÜMÖRÜ SEGMENTASYONU İÇİN YENİ BİR O-NET MODELİ

Yıl 2025, Cilt: 28 Sayı: 3, 1362 - 1374, 03.09.2025

Süleyman Uzun , Ekin Ekinci , Şevket Ay

https://doi.org/10.17780/ksujes.1674766

Öz

Kanser, küresel ölüm oranlarına önemli ölçüde katkıda bulunan bir hastalık olup, beyin tümörleri en tehlikeli kanser türleri arasında yer almaktadır. Erken teşhis ve uygun tedavi, hastaların yaşam süresi ve yaşam kalitesi üzerinde büyük etkiye sahiptir. Ayrıca, tümör tipinin doğru belirlenmesi, tedavi planlaması ve kişiselleştirilmiş tedavi stratejilerinin oluşturulması açısından kritik öneme sahiptir. Bu nedenle, beyin tümörlerinin türünü belirlemek için güvenilir ve etkili bir görüntüleme ve analiz yöntemine ihtiyaç vardır. Son yıllarda, derin öğrenme yöntemlerindeki ilerlemeler tıbbi görüntüleme alanında önemli gelişmelere yol açmıştır. Bu çalışmada, beyin tümörlerinin segmentasyonunu otomatikleştirmek için özel olarak tasarlanan O-Net adlı derin öğrenme modeli tanıtılmaktadır. U-Net mimarisini temel alan O-Net, gelişmiş yapısı ve derinliği sayesinde üstün performans sergilemektedir. Beyin Manyetik Rezonans Görüntüleri (MRI) kullanılarak yapılan deneysel değerlendirmelerde, O-Net modelinin literatürdeki diğer çalışmalara kıyasla daha yüksek performans gösterdiği belirlenmiştir. Model, %90 Dice skoru ile yüksek doğruluk oranına ulaşmıştır. Elde edilen sonuçlar, O-Net modelinin beyin tümörlerinin doğru tespiti ve segmentasyonu için güvenilir bir araç olduğunu göstermektedir. O-Net’in gelişmiş versiyonu, gelecekte klinik uygulamalarda beyin tümörlerinin tanı ve tedavisinde etkili bir araç olarak kullanılabilir.

Anahtar Kelimeler

Beyin tümörü medikal görüntüleme otomatik segmentasyon derin öğrenme O-Net

Kaynakça

  • AboElenein, N. M., Songhao, P., & Afifi, A. (2022). IRDNU-Net: Inception residual dense nested u-net for brain tumor segmentation. Multimedia Tools and Applications, 81(17), 24041-24057. https://doi.org/10.1007/s11042-022-12586-9
  • Agarwala, S., Sharma, S., & Shankar, B. U. (2022, July). A-UNet: Attention 3D UNet architecture for multiclass segmentation of Brain Tumor. In 2022 IEEE Region 10 Symposium (TENSYMP) (pp. 1-5). IEEE. https://doi.org/10.1109/TENSYMP54529.2022.9864546
  • Aghabiglou, A., & Eksioglu, E. M. (2021). Projection-Based cascaded U-Net model for MR image reconstruction. Computer Methods and Programs in Biomedicine, 207, 106151. https://doi.org/10.1016/j.cmpb.2021.106151
  • Aledhari, M., & Razzak, R. (2020, December). An adaptive segmentation technique to detect brain tumors using 2D Unet. In 2020 IEEE International Conference on bioinformatics and biomedicine (BIBM) (pp. 2328-2334). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313547
  • Alqazzaz, S., Sun, X., Yang, X., & Nokes, L. (2019). Automated brain tumor segmentation on multi-modal MR image using SegNet. Computational visual media, 5, 209-219. https://doi.org/10.1007/s41095-019-0139-y
  • Anaya-Isaza, A., Mera-Jiménez, L., & Fernandez-Quilez, A. (2023). CrossTransUnet: a new computationally inexpensive tumor segmentation model for brain MRI. IEEE Access, 11, 27066-27085. https://doi.org/10.1109/ACCESS.2023.3257767
  • 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
  • Asif, S., Wenhui, Y., ur-Rehman, S., ul-ain, Q., Amjad, K., Yueyang, Y., ... & Awais, M. (2025). Advancements and prospects of machine learning in medical diagnostics: unveiling the future of diagnostic precision. Archives of Computational Methods in Engineering, 32, 853–883. https://doi.org/10.1007/s11831-024-10148-w
  • Ateş, M. U., Günlü, R. T., Ekinci, E., & Garip, Z. (2024). Using 3D U-Net for Brain Tumour Segmentation from Magnetic Resonance Images. Academic Platform Journal of Engineering and Smart Systems, 12(3), 81-87. https://doi.org/10.21541/apjess.1508913
  • Banan, A., Nasiri, A., & Taheri-Garavand, A. (2020). Deep learning-based appearance features extraction for automated carp species identification. Aquacultural Engineering, 89, 102053. https://doi.org/10.1016/j.aquaeng.2020.10205
  • Chang, J., Zhang, X., Ye, M., Huang, D., Wang, P., & Yao, C. (2018, October). Brain tumor segmentation based on 3D Unet with multi-class focal loss. In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-5). IEEE. https://doi.org/10.1109/CISP-BMEI.2018.8633056
  • Chaturvedi, M., Rashid, M. A., & Paliwal, K. K. (2025). RNA structure prediction using deep learning—A comprehensive review. Computers in Biology and Medicine, 188, 109845. https://doi.org/10.1016/j.compbiomed.2025.109845
  • Chen, F., Ding, Y., Wu, Z., Wu, D., & Wen, J. (2018, December). An improved framework called Du++ applied to brain tumor segmentation. In 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 85-88). IEEE. https://doi.org/10.1109/ICCWAMTIP.2018.8632559
  • Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., ... & Prior, F. (2013). The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging, 26, 1045-1057. https://doi.org/10.1007/s10278-013-9622-7
  • De, A., Tiwari, M., Grisan, E., & Chowdhury, A. S. (2022, July). A deep graph cut model for 3D brain tumor segmentation. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2105-2109). IEEE. https://doi.org/10.1109/EMBC48229.2022.9871685
  • Fan, Y., Xu, K., Wu, H., Zheng, Y., & Tao, B. (2020). Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network. IEEE Access, 8, 25111-25121. https://doi.org/10.1109/ACCESS.2020.2970836
  • Han, K., Wang, J., Chu, Y., Liao, Q., Ding, Y., Zheng, D., ... & Zou, Q. (2024). Deep learning based method for predicting DNA N6-methyladenosine sites. Methods, 230, 91-98. https://doi.org/10.1016/j.ymeth.2024.07.012
  • Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., ... & Xu, D. (2022). Unetr: Transformers for 3d medical image segmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 574-584).
  • Hou, A., Wu, L., Sun, H., Yang, Q., Ji, H., Cui, B., & Ji, P. (2021, August). Brain segmentation based on UNet++ with weighted parameters and convolutional neural network. In 2021 IEEE International conference on advances in electrical engineering and computer applications (AEECA) (pp. 644-648). IEEE. https://doi.org/10.1109/AEECA52519.2021.9574279
  • Huang, W., & Wang, J. (2022, March). Automatic Segmentation of brain tumors based on DFP-UNet. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) (Vol. 6, pp. 1304-1307). IEEE. https://doi.org/10.1109/ITOEC53115.2022.9734456
  • Ilyas, T., Khan, A., Umraiz, M., & Kim, H. (2020). Seek: A framework of superpixel learning with cnn features for unsupervised segmentation. Electronics, 9(3), 383. https://doi.org/10.3390/electronics9030383
  • Jena, B., Jain, S., Nayak, G. K., & Saxena, S. (2023). Analysis of depth variation of U-NET architecture for brain tumor segmentation. Multimedia Tools and Applications, 82(7), 10723-10743. https://doi.org/10.1007/s11042-022-13730-1
  • Karimzadeh, R., Rajabi, N., Khodabakhsh, A., Taghavi, F., Fatemizadeh, E., Arabi, H., & Zaidi, H. (2021a, October). X-Net: A novel deep learning architecture with high-resolution feature maps for image segmentation. In 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (pp. 1-3). IEEE. https://doi.org/10.1109/NSS/MIC44867.2021.9875455
  • Karimzadeh, R., Fatemizadeh, E., & Arabi, H. (2021b, November). Attention-based deep learning segmentation: Application to brain tumor delineation. In 2021 28th National and 6th International Iranian Conference on Biomedical Engineering (ICBME) (pp. 248-252). IEEE. https://doi.org/10.1109/ICBME54433.2021.9750374
  • Kermi, A., Mahmoudi, I., & Khadir, M. T. (2019). Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4 (pp. 37-48). Springer International Publishing. https://doi.org/10.1007/978-3-030-11726-9_4
  • Li, G., Zhao, B., Su, X., Yang, Y., Zeng, Z., Hu, P., & Hu, L. (2025). Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification sites. Computers in Biology and Medicine, 186, 109625. https://doi.org/10.1016/j.compbiomed.2024.109625
  • Li, S., Liu, J., & Song, Z. (2022). Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net. International Journal of Machine Learning and Cybernetics, 13(9), 2435-2445. https://doi.org/10.1007/s13042-022-01536-4
  • Lin, R., Wang, S., Chen, Q., Cai, Z., Zhu, Y., & Hu, Y. (2021, March). Combining K-means attention and hierarchical mimicking strategy for 3D U-Net based brain tumor segmentation. In 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE) (pp. 92-96). IEEE. https://doi.org/10.1109/ICICSE52190.2021.9404138
  • Liu, H., Shen, X., Shang, F., Ge, F., & Wang, F. (2019). CU-Net: Cascaded U-Net with loss weighted sampling for brain tumor segmentation. In Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 4 (pp. 102-111). Springer International Publishing. https://doi.org/10.1007/978-3-030-33226-6_12
  • Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440). https://doi.org/10.1109/CVPR.2015.7298965
  • Luke, G. P., Hoffer-Hawlik, K., Van Namen, A. C., & Shang, R. (2019). O-Net: a convolutional neural network for quantitative photoacoustic image segmentation and oximetry. arXiv preprint arXiv:1911.01935.
  • Mathur, P., Raghuvanshi, A. S., Kumari, A., & Chandra, A. (2023, March). Computer-Aided Diagnosis System for Brain Tumor Classification and Segmentation. In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 547-552). IEEE. https://doi.org/10.1109/SPIN57001.2023.10117179
  • Meng, Z., Fan, Z., Zhao, Z., & Su, F. (2018, July). ENS-Unet: end-to-end noise suppression U-Net for brain tumor segmentation. In 2018 40th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5886-5889). IEEE. https://doi.org/10.1109/EMBC.2018.8513676
  • Mortazavi-Zadeh, S. A., Amini, A., & Soltanian-Zadeh, H. (2022, May). Brain tumor segmentation using U-net and U-net++ networks. In 2022 30th International Conference on Electrical Engineering (ICEE) (pp. 841-845). IEEE. https://doi.org/10.1109/ICEE55646.2022.9827132
  • Nawaz, A., Akram, U., Salam, A. A., Ali, A. R., Rehman, A. U., & Zeb, J. (2021, October). VGG-UNET for brain tumor segmentation and ensemble model for survival prediction. In 2021 International Conference on Robotics and Automation in Industry (ICRAI) (pp. 1-6). IEEE. https://doi.org/10.1109/ICRAI54018.2021.9651367
  • Nguyen-Tat, T. B., Hung, T. Q., Nam, P. T., & Ngo, V. M. (2025). Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities. Alexandria Engineering Journal, 119, 558-586. https://doi.org/10.1016/j.aej.2025.01.090
  • Noori, M., Bahri, A., & Mohammadi, K. (2019, October). Attention-guided version of 2D UNet for automatic brain tumor segmentation. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 269-275). IEEE. https://doi.org/10.1109/ICCKE48569.2019.8964956
  • Poonguzhali, R., Ahmad, S., Sivasankar, P. T., Babu, S. A., Joshi, P., Joshi, G. P., & Kim, S. W. (2023). Automated brain tumor diagnosis using deep residual u-net segmentation model. Computers, Materials & Continua, 74(1), 2179-2194. https://doi.org/10.32604/cmc.2023.032816
  • Pravitasari, A. A., Iriawan, N., Almuhayar, M., Azmi, T., Irhamah, I., Fithriasari, K., ... & Ferriastuti, W. (2020). UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1310-1318. http://doi.org/10.12928/telkomnika.v18i3.14753
  • Raza, R., Bajwa, U. I., Mehmood, Y., Anwar, M. W., & Jamal, M. H. (2023). dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomedical Signal Processing and Control, 79, 103861. https://doi.org/10.1016/j.bspc.2022.103861
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28
  • Ru, Q., Chen, G., & Tang, Z. (2021, August). Brain tumor image segmentation method based on M-Unet network. In 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) (pp. 243-246). IEEE. https://doi.org/10.1109/PRAI53619.2021.9551069
  • Shamshirband, S., Rabczuk, T., & Chau, K. W. (2019). A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access, 7, 164650-164666. https://doi.org/10.1109/ACCESS.2019.2951750
  • Sun, M., Wang, J., & Chi, Z. (2020, December). Brain tumor segmentation based on AMRUNet++ neural network. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (pp. 1920-1924). IEEE. https://doi.org/10.1109/ICCC51575.2020.9344915
  • Sun, J., Li, J., & Liu, L. (2021). Semantic segmentation of brain tumor with nested residual attention networks. Multimedia Tools and Applications, 80, 34203-34220. https://doi.org/10.1007/s11042-020-09840-3
  • ur Rehman, M., Khan, S. H., Rizvi, S. D., Abbas, Z., & Zafar, A. (2018, July). Classification of skin lesion by interference of segmentation and convolotion neural network. In 2018 2nd International Conference on Engineering Innovation (ICEI) (pp. 81-85). IEEE. https://doi.org/10.1109/ICEI18.2018.8448814
  • Vittikop, B. S., & Dhotre, S. R. (2019, July). Automatic segmentation of MRI images for brain tumor using unet. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 507-511). IEEE. https://doi.org/10.1109/ICAIT47043.2019.8987265
  • Wang, J., Wang, S., & Zhang, Y. (2025). Deep learning on medical image analysis. CAAI Transactions on Intelligence Technology, 10(1), 1-35. https://doi.org/10.1049/cit2.12356
  • Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R., Ozenberger, B. A., Ellrott, K., ... & Stuart, J. M. (2013). The cancer genome atlas pan-cancer analysis project. Nature genetics, 45(10), 1113-1120. https://doi.org/10.1038/ng.2764
  • Yu, B., Zhou, L., Wang, L., Yang, W., Yang, M., Bourgeat, P., & Fripp, J. (2021). SA-LuT-Nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation. IEEE Transactions on Medical Imaging, 40(5), 1417-1427. https://doi.org/10.1109/TMI.2021.3056678
  • Zhang, L., Lan, C., Fu, L., Mao, X., & Zhang, M. (2023). Segmentation of brain tumor MRI image based on improved attention module Unet network. Signal, Image and Video Processing, 17(5), 2277-2285. https://doi.org/10.1007/s11760-022-02443-5

A NEW O-NET MODEL FOR AUTOMATED BRAIN TUMOR SEGMENTATION ON MAGNETIC RESONANCE IMAGES

Yıl 2025, Cilt: 28 Sayı: 3, 1362 - 1374, 03.09.2025

Süleyman Uzun , Ekin Ekinci , Şevket Ay

https://doi.org/10.17780/ksujes.1674766

Öz

Cancer is a major contributor to global mortality rates, with brain tumors being among the most dangerous types. Early diagnosis and appropriate treatment significantly impact patients' survival rates and quality of life. Moreover, accurately determining the tumor type is crucial for treatment planning and the development of personalized treatment strategies. Therefore, a reliable and effective imaging and analysis method is required to classify brain tumors accurately. In recent years, advancements in deep learning techniques have led to significant progress in medical imaging. This study introduces O-Net, a deep learning model specifically designed to automate brain tumor segmentation. Based on the U-Net architecture, O-Net exhibits a sophisticated structure and depth, enabling superior performance. Experimental evaluations using brain Magnetic Resonance Imaging (MRI) revealed that the O-Net model outperformed other studies in the literature. The model achieved a high accuracy rate with a 90% Dice score. These results demonstrate that O-Net is a reliable tool for the accurate detection and segmentation of brain tumors. The advanced version of the O-Net model can be effectively utilized in future clinical applications for the diagnosis and treatment of brain tumors.

Anahtar Kelimeler

Brain tumor medical imaging automatic segmentation deep learning O-Net

Kaynakça

  • AboElenein, N. M., Songhao, P., & Afifi, A. (2022). IRDNU-Net: Inception residual dense nested u-net for brain tumor segmentation. Multimedia Tools and Applications, 81(17), 24041-24057. https://doi.org/10.1007/s11042-022-12586-9
  • Agarwala, S., Sharma, S., & Shankar, B. U. (2022, July). A-UNet: Attention 3D UNet architecture for multiclass segmentation of Brain Tumor. In 2022 IEEE Region 10 Symposium (TENSYMP) (pp. 1-5). IEEE. https://doi.org/10.1109/TENSYMP54529.2022.9864546
  • Aghabiglou, A., & Eksioglu, E. M. (2021). Projection-Based cascaded U-Net model for MR image reconstruction. Computer Methods and Programs in Biomedicine, 207, 106151. https://doi.org/10.1016/j.cmpb.2021.106151
  • Aledhari, M., & Razzak, R. (2020, December). An adaptive segmentation technique to detect brain tumors using 2D Unet. In 2020 IEEE International Conference on bioinformatics and biomedicine (BIBM) (pp. 2328-2334). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313547
  • Alqazzaz, S., Sun, X., Yang, X., & Nokes, L. (2019). Automated brain tumor segmentation on multi-modal MR image using SegNet. Computational visual media, 5, 209-219. https://doi.org/10.1007/s41095-019-0139-y
  • Anaya-Isaza, A., Mera-Jiménez, L., & Fernandez-Quilez, A. (2023). CrossTransUnet: a new computationally inexpensive tumor segmentation model for brain MRI. IEEE Access, 11, 27066-27085. https://doi.org/10.1109/ACCESS.2023.3257767
  • 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
  • Asif, S., Wenhui, Y., ur-Rehman, S., ul-ain, Q., Amjad, K., Yueyang, Y., ... & Awais, M. (2025). Advancements and prospects of machine learning in medical diagnostics: unveiling the future of diagnostic precision. Archives of Computational Methods in Engineering, 32, 853–883. https://doi.org/10.1007/s11831-024-10148-w
  • Ateş, M. U., Günlü, R. T., Ekinci, E., & Garip, Z. (2024). Using 3D U-Net for Brain Tumour Segmentation from Magnetic Resonance Images. Academic Platform Journal of Engineering and Smart Systems, 12(3), 81-87. https://doi.org/10.21541/apjess.1508913
  • Banan, A., Nasiri, A., & Taheri-Garavand, A. (2020). Deep learning-based appearance features extraction for automated carp species identification. Aquacultural Engineering, 89, 102053. https://doi.org/10.1016/j.aquaeng.2020.10205
  • Chang, J., Zhang, X., Ye, M., Huang, D., Wang, P., & Yao, C. (2018, October). Brain tumor segmentation based on 3D Unet with multi-class focal loss. In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-5). IEEE. https://doi.org/10.1109/CISP-BMEI.2018.8633056
  • Chaturvedi, M., Rashid, M. A., & Paliwal, K. K. (2025). RNA structure prediction using deep learning—A comprehensive review. Computers in Biology and Medicine, 188, 109845. https://doi.org/10.1016/j.compbiomed.2025.109845
  • Chen, F., Ding, Y., Wu, Z., Wu, D., & Wen, J. (2018, December). An improved framework called Du++ applied to brain tumor segmentation. In 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 85-88). IEEE. https://doi.org/10.1109/ICCWAMTIP.2018.8632559
  • Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., ... & Prior, F. (2013). The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging, 26, 1045-1057. https://doi.org/10.1007/s10278-013-9622-7
  • De, A., Tiwari, M., Grisan, E., & Chowdhury, A. S. (2022, July). A deep graph cut model for 3D brain tumor segmentation. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2105-2109). IEEE. https://doi.org/10.1109/EMBC48229.2022.9871685
  • Fan, Y., Xu, K., Wu, H., Zheng, Y., & Tao, B. (2020). Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network. IEEE Access, 8, 25111-25121. https://doi.org/10.1109/ACCESS.2020.2970836
  • Han, K., Wang, J., Chu, Y., Liao, Q., Ding, Y., Zheng, D., ... & Zou, Q. (2024). Deep learning based method for predicting DNA N6-methyladenosine sites. Methods, 230, 91-98. https://doi.org/10.1016/j.ymeth.2024.07.012
  • Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., ... & Xu, D. (2022). Unetr: Transformers for 3d medical image segmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 574-584).
  • Hou, A., Wu, L., Sun, H., Yang, Q., Ji, H., Cui, B., & Ji, P. (2021, August). Brain segmentation based on UNet++ with weighted parameters and convolutional neural network. In 2021 IEEE International conference on advances in electrical engineering and computer applications (AEECA) (pp. 644-648). IEEE. https://doi.org/10.1109/AEECA52519.2021.9574279
  • Huang, W., & Wang, J. (2022, March). Automatic Segmentation of brain tumors based on DFP-UNet. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) (Vol. 6, pp. 1304-1307). IEEE. https://doi.org/10.1109/ITOEC53115.2022.9734456
  • Ilyas, T., Khan, A., Umraiz, M., & Kim, H. (2020). Seek: A framework of superpixel learning with cnn features for unsupervised segmentation. Electronics, 9(3), 383. https://doi.org/10.3390/electronics9030383
  • Jena, B., Jain, S., Nayak, G. K., & Saxena, S. (2023). Analysis of depth variation of U-NET architecture for brain tumor segmentation. Multimedia Tools and Applications, 82(7), 10723-10743. https://doi.org/10.1007/s11042-022-13730-1
  • Karimzadeh, R., Rajabi, N., Khodabakhsh, A., Taghavi, F., Fatemizadeh, E., Arabi, H., & Zaidi, H. (2021a, October). X-Net: A novel deep learning architecture with high-resolution feature maps for image segmentation. In 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (pp. 1-3). IEEE. https://doi.org/10.1109/NSS/MIC44867.2021.9875455
  • Karimzadeh, R., Fatemizadeh, E., & Arabi, H. (2021b, November). Attention-based deep learning segmentation: Application to brain tumor delineation. In 2021 28th National and 6th International Iranian Conference on Biomedical Engineering (ICBME) (pp. 248-252). IEEE. https://doi.org/10.1109/ICBME54433.2021.9750374
  • Kermi, A., Mahmoudi, I., & Khadir, M. T. (2019). Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4 (pp. 37-48). Springer International Publishing. https://doi.org/10.1007/978-3-030-11726-9_4
  • Li, G., Zhao, B., Su, X., Yang, Y., Zeng, Z., Hu, P., & Hu, L. (2025). Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification sites. Computers in Biology and Medicine, 186, 109625. https://doi.org/10.1016/j.compbiomed.2024.109625
  • Li, S., Liu, J., & Song, Z. (2022). Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net. International Journal of Machine Learning and Cybernetics, 13(9), 2435-2445. https://doi.org/10.1007/s13042-022-01536-4
  • Lin, R., Wang, S., Chen, Q., Cai, Z., Zhu, Y., & Hu, Y. (2021, March). Combining K-means attention and hierarchical mimicking strategy for 3D U-Net based brain tumor segmentation. In 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE) (pp. 92-96). IEEE. https://doi.org/10.1109/ICICSE52190.2021.9404138
  • Liu, H., Shen, X., Shang, F., Ge, F., & Wang, F. (2019). CU-Net: Cascaded U-Net with loss weighted sampling for brain tumor segmentation. In Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 4 (pp. 102-111). Springer International Publishing. https://doi.org/10.1007/978-3-030-33226-6_12
  • Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440). https://doi.org/10.1109/CVPR.2015.7298965
  • Luke, G. P., Hoffer-Hawlik, K., Van Namen, A. C., & Shang, R. (2019). O-Net: a convolutional neural network for quantitative photoacoustic image segmentation and oximetry. arXiv preprint arXiv:1911.01935.
  • Mathur, P., Raghuvanshi, A. S., Kumari, A., & Chandra, A. (2023, March). Computer-Aided Diagnosis System for Brain Tumor Classification and Segmentation. In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 547-552). IEEE. https://doi.org/10.1109/SPIN57001.2023.10117179
  • Meng, Z., Fan, Z., Zhao, Z., & Su, F. (2018, July). ENS-Unet: end-to-end noise suppression U-Net for brain tumor segmentation. In 2018 40th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5886-5889). IEEE. https://doi.org/10.1109/EMBC.2018.8513676
  • Mortazavi-Zadeh, S. A., Amini, A., & Soltanian-Zadeh, H. (2022, May). Brain tumor segmentation using U-net and U-net++ networks. In 2022 30th International Conference on Electrical Engineering (ICEE) (pp. 841-845). IEEE. https://doi.org/10.1109/ICEE55646.2022.9827132
  • Nawaz, A., Akram, U., Salam, A. A., Ali, A. R., Rehman, A. U., & Zeb, J. (2021, October). VGG-UNET for brain tumor segmentation and ensemble model for survival prediction. In 2021 International Conference on Robotics and Automation in Industry (ICRAI) (pp. 1-6). IEEE. https://doi.org/10.1109/ICRAI54018.2021.9651367
  • Nguyen-Tat, T. B., Hung, T. Q., Nam, P. T., & Ngo, V. M. (2025). Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities. Alexandria Engineering Journal, 119, 558-586. https://doi.org/10.1016/j.aej.2025.01.090
  • Noori, M., Bahri, A., & Mohammadi, K. (2019, October). Attention-guided version of 2D UNet for automatic brain tumor segmentation. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 269-275). IEEE. https://doi.org/10.1109/ICCKE48569.2019.8964956
  • Poonguzhali, R., Ahmad, S., Sivasankar, P. T., Babu, S. A., Joshi, P., Joshi, G. P., & Kim, S. W. (2023). Automated brain tumor diagnosis using deep residual u-net segmentation model. Computers, Materials & Continua, 74(1), 2179-2194. https://doi.org/10.32604/cmc.2023.032816
  • Pravitasari, A. A., Iriawan, N., Almuhayar, M., Azmi, T., Irhamah, I., Fithriasari, K., ... & Ferriastuti, W. (2020). UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1310-1318. http://doi.org/10.12928/telkomnika.v18i3.14753
  • Raza, R., Bajwa, U. I., Mehmood, Y., Anwar, M. W., & Jamal, M. H. (2023). dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomedical Signal Processing and Control, 79, 103861. https://doi.org/10.1016/j.bspc.2022.103861
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28
  • Ru, Q., Chen, G., & Tang, Z. (2021, August). Brain tumor image segmentation method based on M-Unet network. In 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) (pp. 243-246). IEEE. https://doi.org/10.1109/PRAI53619.2021.9551069
  • Shamshirband, S., Rabczuk, T., & Chau, K. W. (2019). A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access, 7, 164650-164666. https://doi.org/10.1109/ACCESS.2019.2951750
  • Sun, M., Wang, J., & Chi, Z. (2020, December). Brain tumor segmentation based on AMRUNet++ neural network. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (pp. 1920-1924). IEEE. https://doi.org/10.1109/ICCC51575.2020.9344915
  • Sun, J., Li, J., & Liu, L. (2021). Semantic segmentation of brain tumor with nested residual attention networks. Multimedia Tools and Applications, 80, 34203-34220. https://doi.org/10.1007/s11042-020-09840-3
  • ur Rehman, M., Khan, S. H., Rizvi, S. D., Abbas, Z., & Zafar, A. (2018, July). Classification of skin lesion by interference of segmentation and convolotion neural network. In 2018 2nd International Conference on Engineering Innovation (ICEI) (pp. 81-85). IEEE. https://doi.org/10.1109/ICEI18.2018.8448814
  • Vittikop, B. S., & Dhotre, S. R. (2019, July). Automatic segmentation of MRI images for brain tumor using unet. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 507-511). IEEE. https://doi.org/10.1109/ICAIT47043.2019.8987265
  • Wang, J., Wang, S., & Zhang, Y. (2025). Deep learning on medical image analysis. CAAI Transactions on Intelligence Technology, 10(1), 1-35. https://doi.org/10.1049/cit2.12356
  • Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R., Ozenberger, B. A., Ellrott, K., ... & Stuart, J. M. (2013). The cancer genome atlas pan-cancer analysis project. Nature genetics, 45(10), 1113-1120. https://doi.org/10.1038/ng.2764
  • Yu, B., Zhou, L., Wang, L., Yang, W., Yang, M., Bourgeat, P., & Fripp, J. (2021). SA-LuT-Nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation. IEEE Transactions on Medical Imaging, 40(5), 1417-1427. https://doi.org/10.1109/TMI.2021.3056678
  • Zhang, L., Lan, C., Fu, L., Mao, X., & Zhang, M. (2023). Segmentation of brain tumor MRI image based on improved attention module Unet network. Signal, Image and Video Processing, 17(5), 2277-2285. https://doi.org/10.1007/s11760-022-02443-5
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme
Bölüm Bilgisayar Mühendisliği
Yazarlar

Süleyman Uzun 0000-0001-8246-6733

Ekin Ekinci 0000-0003-0658-592X

Şevket Ay 0000-0001-8422-4615

Yayımlanma Tarihi 3 Eylül 2025
Gönderilme Tarihi 12 Nisan 2025
Kabul Tarihi 21 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 28 Sayı: 3

Kaynak Göster

APA Uzun, S., Ekinci, E., & Ay, Ş. (2025). A NEW O-NET MODEL FOR AUTOMATED BRAIN TUMOR SEGMENTATION ON MAGNETIC RESONANCE IMAGES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1362-1374. https://doi.org/10.17780/ksujes.1674766
 Kapak Resmi İndir