COMPUTER-AIDED DETECTION OF BRAIN TUMORS USING IMAGE PROCESSING TECHNIQUES

Hilal Güven; Ahmet Saygılı

doi:10.17780/ksujes.1447899

Araştırma Makalesi

GÖRÜNTÜ İŞLEME TEKNIKLERI KULLANARAK BEYIN TÜMÖRLERININ BILGISAYAR DESTEKLI TESPITI

Yıl 2024, , 999 - 1018, 03.09.2024

Hilal Güven Ahmet Saygılı

https://doi.org/10.17780/ksujes.1447899

Öz

Beyin tümörleri, beyindeki hücrelerin kontrolsüz çoğalmasıyla oluşan kitlelerdir. Beyin tümörleri malign veya benign olabilirler ve erken aşamada doğru bir şekilde tanımlanmazsa ölümcül olabilirler. Bilgisayarlı görü işleme, erken teşhis, tedavi yanıtının izlenmesi ve tümör sınıflandırması için kullanılır. Çalışmada görüntü işleme teknikleri kullanılarak günümüzün önemli bir hastalık olan beyin tümörlerini tespit etmek amaçlanmaktadır. Bu doğrultuda, 253 görüntüden oluşan veri kümesi üzerinde önişleme teknikleri ve veri artırma teknikleri uygulanmıştır. Beyin tümörlerinin tespiti için öncelikle CNN kullanılmıştır ancak daha iyi sonuçlar elde etmek için transfer öğrenme yöntemi kullanılmıştır. Beyin tümörlerinin tespiti için önceden eğitilmiş olan VGG-16, DenseNet-121, ResNet-50, MobileNet_V2 mimarileri kullanılmıştır. Transfer öğrenme ile model, beyin tümörü tespiti için özelleştirilmiş bir çıkış katmanı ekleyerek daha az veri ile daha iyi performans elde edilmiştir. Deneyler sonucunda en iyi oranları VGG-16 mimarisi ile veri artırma öncesi %84.61, veri artırma sonrasında %92.31 doğruluk oranı elde edilmiştir. Diğer çalışmalarla karşılaştırıldığında, veri artırma sonrası elde edilen doğruluk oranının birçok çalışmadan daha iyi olduğu gözlemlenmiştir. Çalışmada ayrıca diğer derin öğrenme mimarilerinden elde edilen sonuçlar karşılaştırılarak çalışmada ayrıca sunulmuştur. Ayrıca, çeşitli tümör kategorilerindeki mevcut teknolojik ilerlemelerin özetlenmesi, araştırmacıların gelecekteki eğilimleri anlamalarına yardımcı olabilir.

Anahtar Kelimeler

Beyin tümörü, bilgisayar destekli teşhis, VGG-16, DenseNet-121, ResNet-50, MobileNet_V2

Kaynakça

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G. & Isard, M. (2016). TensorFlow: a system for Large-Scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).
Aktürk, S. & Serbest, K. (2022). Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. Journalof Smart Systems Research, 3(2), 97-119
Alhalim, A., Abd Alrahman, G., Hussain Hassan, N. M. & Nashat, A. A. (2024). Computer-Aided Diagnosis And Detection For Brain Cancer. Fayoum University Journal of Engineering, 7(1), 49-62. https://doi.org/10.21608/FUJE.2023.221477.1052
Arslan, Ö. & Uymaz, S. A. (2022). Classification of Invoice Images By Using Convolutional Neural Networks. Journal of Advanced Research in Natural and Applied Sciences, 8(1), 8-25. https://doi.org/10.28979/jarnas.953634
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
Atallah, O., Badary, A., Almealawy, Y. F., Sanker, V., Awuah, W. A., Abdul-Rahman, T., Alrubaye, S. N. & Chaurasia, B. (2024). Non-colloid-cyst primary brain tumors: A systematic review of unexpected fatality. Journal of Clinical Neuroscience, 119, 129-140. https://doi.org/10.1016/j.jocn.2023.11.022
Badža, M. M. & Barjaktarović, M. Č. (2020). Classification of brain tumors from MRI images using a convolutional neural network. Applied Sciences, 10(6), 1999. https://doi.org/10.3390/app10061999
Chan, H. P., Hadjiiski, L. M. & Samala, R. K. (2020). Computer‐aided diagnosis in the era of deep learning. Medical physics, 47(5), e218-e227. https://doi.org/10.1002/mp.13764
Chanu, M. M., Singh, N. H., Muppala, C., Prabu, R. T., Singh, N. P. & Thongam, K. (2023). Computer-aided detection and classification of brain tumor using YOLOv3 and deep learning. Soft Computing, 27(14), 9927-9940. https://doi.org/10.1007/s00500-023-08343-1
Chattopadhyay, A. & Maitra, M. (2022). MRI-based brain tumour image detection using CNN based deep learning method. Neuroscience informatics, 2(4), 100060. https://doi.org/10.1016/j.neuri.2022.100060
Aslan, T. & Çakı, E. E. (2023). Beyin tümörü teşhisinde CNN-FL modeli ağ performansının aktivasyon fonksiyonlarına göre karşılaştırılması. Journal of Scientific Reports-B, 008, 43-54.
Dertat, A. (2017). Applied deep learning-part 4: Convolutional neural networks. Towards Data Science, 26. https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2 Accessed 14.02.2024.
Doğan, F. & Türkoğlu, (2019). Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 409-445. https://doi.org/10.24012/dumf.411130.
Doğan, F. & Türkoğlu, İ. (2018). "Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması." Sakarya University Journal of Computer and Information Sciences, 1(1), 10-21.
Dubey, N., Bhagat, E., Rana, S., & Pathak, K. (2022). A novel approach to detect plant disease using DenseNet-121 neural network. In Smart Trends in Computing and Communications: Proceedings of SmartCom 2022 (pp. 63-74). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9967-2_7
Er, M. B. (2021). Önceden Eğitilmiş Derin Ağlar İle Göğüs Röntgeni Görüntüleri Kullanarak Pnömoni Siniflandirilmasi. Konya Journal of Engineering Sciences, 9(1), 193-204. https://doi.org/10.36306/konjes.794505
García-Ordás, M. T., Benítez-Andrades, J. A., García-Rodríguez, I., Benavides, C. and Alaiz-Moretón, H. (2020). Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data. Sensors, 20(4), 1214. https://doi.org/10.3390/s20041214
Govindaraj, S. and Sandhiya, G. Brain Tumor Detection Using Convolutional Neural Network With Image Processing. 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). IEEE. https://www.doi.org/10.56726/irjmets38709
Hossain, T., Shishir, F. S., Ashraf, M., Al Nasim, M. A. and Shah, F. M. (2019). Brain tumor detection using convolutional neural network. 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), 1-6. IEEE.
Hu, M., Zhong, Y., Xie, S., Lv, H. and Lv, Z. (2021). Fuzzy system based medical image processing for brain disease prediction. Frontiers in Neuroscience, 15, 714318. https://doi.org/10.3389/fnins.2021.714318
Indraswari, R., Rokhana, R., & Herulambang, W. (2022). Melanoma image classification based on MobileNetV2 network. Procedia computer science, 197, 198-207. https://doi.org/10.1016/j.procs.2021.12.132 Karabay, A. Keras Nedir? [What is Keras?]. Retrieved from https://www.karabayyazilim.com/blog/python/keras-nedir-2020-02-08-225241 Accessed 08.02.2024
Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., Mosavi, A. and Dehzangi, I. (2022). Accurate brain tumor detection using deep convolutional neural network. Computational and Structural Biotechnology Journal, 20, 4733-4745. https://doi.org/10.1016/j.csbj.2022.08.039
Kumar, G., Kumar, P. and Kumar, D. (2021). Brain tumor detection using convolutional neural network. 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-6). IEEE. https://doi.org/10.1109/ICMNWC52512.2021.9688460
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
Macdonald, D., & Engelhardt, G. (2010). 2.39 predictive modeling of corrosion. Shreir’s Corros, Elsevier BV, 2, 1630-1679.
Madhuri, G. S., Mahesh, T. and Vivek, V. (2022). A novel approach for automatic brain tumor detection using machine learning algorithms. In Big data management in Sensing (pp. 87-101). River Publishers. https://doi.org/10.1201/9781003337355-7 Mishra, M. (2020). Convolutional neural networks, explained. Towards Data Science, 26. https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939 Accessed 14.02.24.
Nikmanesh, Y., Mohammadi, M. J., Yousefi, H., Mansourimoghadam, S., & Taherian, M. (2023). The effect of long-term exposure to toxic air pollutants on the increased risk of malignant brain tumors. Reviews on Environmental Health, 38(3), 519-530. https://doi.org/10.1515/reveh-2022-0033
Ostrom, Q. T., Cioffi, G., Waite, K., Kruchko, C., & Barnholtz-Sloan, J. S. (2021). CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014–2018. Neuro-oncology, 23(Supplement_3), iii1-iii105. https://doi.org/10.1093/neuonc/noab200
Rai, H. M., & Chatterjee, K. (2021). 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multimedia Tools and Applications, 80, 36111-36141. https://doi.org/10.1007/s11042-021-11504-9
Sadad, T., Rehman, A., Munir, A., Saba, T., Tariq, U., Ayesha, N., & Abbasi, R. (2021). Brain tumor detection and multi‐classification using advanced deep learning techniques. Microscopy Research and Technique, 84(6), 1296-1308. https://doi.org/10.1002/jemt.23688
Sarkar, S., Kumar, A., Chakraborty, S., Aich, S., Sim, J.-S., & Kim, H.-C. (2020). A CNN based approach for the detection of brain tumor using MRI scans. Test Engineering and Management, 83, 16580-16586.
Saxena, P., Maheshwari, A., & Maheshwari, S. (2020). Predictive modeling of brain tumor: a deep learning approach. In Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020 (pp. 275-285). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-6067-5_30
Siar, M., & Teshnehlab, M. (2019). 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
Sudharson, K., Sermakani, A., Parthipan, V., Dhinakaran, D., Petchiammal, G. E., & Usha, N. (2022). Hybrid Deep Learning Neural System for Brain Tumor Detection. In 2022 2nd International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE. https://doi.org/10.1109/CONIT55038.2022.9847708
Sultan, H. H., Salem, N. M., & Al-Atabany, W. (2019). Multi-classification of brain tumor images using deep neural network. IEEE access, 7, 69215-69225. 10.1109/ACCESS.2019.2919122
Vaibhav, R. Fully Connected Layer. Medium. https://medium.com/@vaibhav1403/fully-connected-layer-f13275337c7c Accessed 10.02.24
Zafar, A., Aamir, M., Mohd Nawi, N., Arshad, A., Riaz, S., Alruban, A., Dutta, A. K., & Almotairi, S. (2022). A comparison of pooling methods for convolutional neural networks. Applied Sciences, 12(17), 8643. https://doi.org/10.3390/app12178643
Zailan, Z. N., Mostafa, S. A., Abdulmaged, A. I., Baharum, Z., Jaber, M. M., & Hidayat, R. (2022). Deep Learning Approach for Prediction of Brain Tumor from Small Number of MRI Images. JOIV: International Journal on Informatics Visualization, 6(2-2), 581-586.

COMPUTER-AIDED DETECTION OF BRAIN TUMORS USING IMAGE PROCESSING TECHNIQUES

Yıl 2024, , 999 - 1018, 03.09.2024

Hilal Güven Ahmet Saygılı

https://doi.org/10.17780/ksujes.1447899

Öz

Brain tumors are masses formed by the uncontrolled proliferation of cells in the brain. Brain tumors can be malignant or benign and can be fatal if not accurately identified at an early stage. Computer vision processing is used for early diagnosis, monitoring treatment response, and tumor classification. This study aims to detect brain tumors, a significant disease of our time, using image processing techniques. Preprocessing and data augmentation techniques were applied to a dataset of 253 images. Initially, CNNs were used for tumor detection, but transfer learning was employed for better results. Pre-trained VGG-16, DenseNet-121, ResNet-50, and MobileNet_V2 architectures were used. The model, adapted with transfer learning, achieved better performance with less data by adding a customized output layer for brain tumor detection. Experiments showed the best results with VGG-16, achieving 84.61% accuracy before data augmentation and 92.31% after augmentation. Compared to other studies, the post-augmentation accuracy rate was observed to be better than many others. The study also compares results from other deep learning architectures. Summarizing the current technological advancements in various tumor categories may help researchers understand future trends.

Anahtar Kelimeler

Brain tumor, computer-aided detection, VGG-16, DenseNet-121, ResNet-50, MobileNet_V2

Kaynakça

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G. & Isard, M. (2016). TensorFlow: a system for Large-Scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).
Aktürk, S. & Serbest, K. (2022). Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. Journalof Smart Systems Research, 3(2), 97-119
Alhalim, A., Abd Alrahman, G., Hussain Hassan, N. M. & Nashat, A. A. (2024). Computer-Aided Diagnosis And Detection For Brain Cancer. Fayoum University Journal of Engineering, 7(1), 49-62. https://doi.org/10.21608/FUJE.2023.221477.1052
Arslan, Ö. & Uymaz, S. A. (2022). Classification of Invoice Images By Using Convolutional Neural Networks. Journal of Advanced Research in Natural and Applied Sciences, 8(1), 8-25. https://doi.org/10.28979/jarnas.953634
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
Atallah, O., Badary, A., Almealawy, Y. F., Sanker, V., Awuah, W. A., Abdul-Rahman, T., Alrubaye, S. N. & Chaurasia, B. (2024). Non-colloid-cyst primary brain tumors: A systematic review of unexpected fatality. Journal of Clinical Neuroscience, 119, 129-140. https://doi.org/10.1016/j.jocn.2023.11.022
Badža, M. M. & Barjaktarović, M. Č. (2020). Classification of brain tumors from MRI images using a convolutional neural network. Applied Sciences, 10(6), 1999. https://doi.org/10.3390/app10061999
Chan, H. P., Hadjiiski, L. M. & Samala, R. K. (2020). Computer‐aided diagnosis in the era of deep learning. Medical physics, 47(5), e218-e227. https://doi.org/10.1002/mp.13764
Chanu, M. M., Singh, N. H., Muppala, C., Prabu, R. T., Singh, N. P. & Thongam, K. (2023). Computer-aided detection and classification of brain tumor using YOLOv3 and deep learning. Soft Computing, 27(14), 9927-9940. https://doi.org/10.1007/s00500-023-08343-1
Chattopadhyay, A. & Maitra, M. (2022). MRI-based brain tumour image detection using CNN based deep learning method. Neuroscience informatics, 2(4), 100060. https://doi.org/10.1016/j.neuri.2022.100060
Aslan, T. & Çakı, E. E. (2023). Beyin tümörü teşhisinde CNN-FL modeli ağ performansının aktivasyon fonksiyonlarına göre karşılaştırılması. Journal of Scientific Reports-B, 008, 43-54.
Dertat, A. (2017). Applied deep learning-part 4: Convolutional neural networks. Towards Data Science, 26. https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2 Accessed 14.02.2024.
Doğan, F. & Türkoğlu, (2019). Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 409-445. https://doi.org/10.24012/dumf.411130.
Doğan, F. & Türkoğlu, İ. (2018). "Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması." Sakarya University Journal of Computer and Information Sciences, 1(1), 10-21.
Dubey, N., Bhagat, E., Rana, S., & Pathak, K. (2022). A novel approach to detect plant disease using DenseNet-121 neural network. In Smart Trends in Computing and Communications: Proceedings of SmartCom 2022 (pp. 63-74). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9967-2_7
Er, M. B. (2021). Önceden Eğitilmiş Derin Ağlar İle Göğüs Röntgeni Görüntüleri Kullanarak Pnömoni Siniflandirilmasi. Konya Journal of Engineering Sciences, 9(1), 193-204. https://doi.org/10.36306/konjes.794505
García-Ordás, M. T., Benítez-Andrades, J. A., García-Rodríguez, I., Benavides, C. and Alaiz-Moretón, H. (2020). Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data. Sensors, 20(4), 1214. https://doi.org/10.3390/s20041214
Govindaraj, S. and Sandhiya, G. Brain Tumor Detection Using Convolutional Neural Network With Image Processing. 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). IEEE. https://www.doi.org/10.56726/irjmets38709
Hossain, T., Shishir, F. S., Ashraf, M., Al Nasim, M. A. and Shah, F. M. (2019). Brain tumor detection using convolutional neural network. 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), 1-6. IEEE.
Hu, M., Zhong, Y., Xie, S., Lv, H. and Lv, Z. (2021). Fuzzy system based medical image processing for brain disease prediction. Frontiers in Neuroscience, 15, 714318. https://doi.org/10.3389/fnins.2021.714318
Indraswari, R., Rokhana, R., & Herulambang, W. (2022). Melanoma image classification based on MobileNetV2 network. Procedia computer science, 197, 198-207. https://doi.org/10.1016/j.procs.2021.12.132 Karabay, A. Keras Nedir? [What is Keras?]. Retrieved from https://www.karabayyazilim.com/blog/python/keras-nedir-2020-02-08-225241 Accessed 08.02.2024
Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., Mosavi, A. and Dehzangi, I. (2022). Accurate brain tumor detection using deep convolutional neural network. Computational and Structural Biotechnology Journal, 20, 4733-4745. https://doi.org/10.1016/j.csbj.2022.08.039
Kumar, G., Kumar, P. and Kumar, D. (2021). Brain tumor detection using convolutional neural network. 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-6). IEEE. https://doi.org/10.1109/ICMNWC52512.2021.9688460
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
Macdonald, D., & Engelhardt, G. (2010). 2.39 predictive modeling of corrosion. Shreir’s Corros, Elsevier BV, 2, 1630-1679.
Madhuri, G. S., Mahesh, T. and Vivek, V. (2022). A novel approach for automatic brain tumor detection using machine learning algorithms. In Big data management in Sensing (pp. 87-101). River Publishers. https://doi.org/10.1201/9781003337355-7 Mishra, M. (2020). Convolutional neural networks, explained. Towards Data Science, 26. https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939 Accessed 14.02.24.
Nikmanesh, Y., Mohammadi, M. J., Yousefi, H., Mansourimoghadam, S., & Taherian, M. (2023). The effect of long-term exposure to toxic air pollutants on the increased risk of malignant brain tumors. Reviews on Environmental Health, 38(3), 519-530. https://doi.org/10.1515/reveh-2022-0033
Ostrom, Q. T., Cioffi, G., Waite, K., Kruchko, C., & Barnholtz-Sloan, J. S. (2021). CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014–2018. Neuro-oncology, 23(Supplement_3), iii1-iii105. https://doi.org/10.1093/neuonc/noab200
Rai, H. M., & Chatterjee, K. (2021). 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multimedia Tools and Applications, 80, 36111-36141. https://doi.org/10.1007/s11042-021-11504-9
Sadad, T., Rehman, A., Munir, A., Saba, T., Tariq, U., Ayesha, N., & Abbasi, R. (2021). Brain tumor detection and multi‐classification using advanced deep learning techniques. Microscopy Research and Technique, 84(6), 1296-1308. https://doi.org/10.1002/jemt.23688
Sarkar, S., Kumar, A., Chakraborty, S., Aich, S., Sim, J.-S., & Kim, H.-C. (2020). A CNN based approach for the detection of brain tumor using MRI scans. Test Engineering and Management, 83, 16580-16586.
Saxena, P., Maheshwari, A., & Maheshwari, S. (2020). Predictive modeling of brain tumor: a deep learning approach. In Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020 (pp. 275-285). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-6067-5_30
Siar, M., & Teshnehlab, M. (2019). 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
Sudharson, K., Sermakani, A., Parthipan, V., Dhinakaran, D., Petchiammal, G. E., & Usha, N. (2022). Hybrid Deep Learning Neural System for Brain Tumor Detection. In 2022 2nd International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE. https://doi.org/10.1109/CONIT55038.2022.9847708
Sultan, H. H., Salem, N. M., & Al-Atabany, W. (2019). Multi-classification of brain tumor images using deep neural network. IEEE access, 7, 69215-69225. 10.1109/ACCESS.2019.2919122
Vaibhav, R. Fully Connected Layer. Medium. https://medium.com/@vaibhav1403/fully-connected-layer-f13275337c7c Accessed 10.02.24
Zafar, A., Aamir, M., Mohd Nawi, N., Arshad, A., Riaz, S., Alruban, A., Dutta, A. K., & Almotairi, S. (2022). A comparison of pooling methods for convolutional neural networks. Applied Sciences, 12(17), 8643. https://doi.org/10.3390/app12178643
Zailan, Z. N., Mostafa, S. A., Abdulmaged, A. I., Baharum, Z., Jaber, M. M., & Hidayat, R. (2022). Deep Learning Approach for Prediction of Brain Tumor from Small Number of MRI Images. JOIV: International Journal on Informatics Visualization, 6(2-2), 581-586.

Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Görüntü İşleme, Derin Öğrenme
Bölüm	Bilgisayar Mühendisliği
Yazarlar	Hilal Güven 0000-0002-7461-4510 Ahmet Saygılı 0000-0001-8625-4842
Yayımlanma Tarihi	3 Eylül 2024
Gönderilme Tarihi	6 Mart 2024
Kabul Tarihi	14 Haziran 2024
Yayımlandığı Sayı	Yıl 2024

Kaynak Göster

APA	Güven, H., & Saygılı, A. (2024). COMPUTER-AIDED DETECTION OF BRAIN TUMORS USING IMAGE PROCESSING TECHNIQUES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 999-1018. https://doi.org/10.17780/ksujes.1447899

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