MRI Verilerinde Tümör Tespiti için Transfer Tabanlı Derin Öğrenme Algoritması Karşılaştırması

Yıl 2024, Cilt: 14 Sayı: 3, 1322 – 1339, 15.09.2024

Cem Demirel , Emel Soylu

https://doi.org/10.31466/kfbd.1455542

Öz

Bu çalışmada, Manyetik Rezonans Görüntüleme (MRG), invazif olmayan doğası ve yüksek çözünürlüklü görüntüleme yetenekleri nedeniyle beyin tümörlerinin teşhisinde hayati bir araç haline gelmiştir. Bu çalışmada, derin öğrenme algoritmalarının performanslarını karşılaştırdık. Kapsamlı bir MRG taramaları veri kümesi, modelimizi eğitmek ve doğrulamak için kullanıldı, bu da çeşitli tümör tipleri ve görüntüleme koşulları için sağlam bir performans sağladı. Sonuçlar, yakalama konusunda yüksek bir doğruluk ve hassasiyet elde ederek yaklaşımımızın etkinliğini göstermektedir. Çalışmamız, nöro görüntüleme alanında erken teşhis ve takip için etkili ve güvenilir araçların geliştirilmesine katkıda bulunmaktadır. Bulgularımız, beyin MRG görüntü sınıflandırma görevleriyle uğraşırken uygun bir derin sinir ağı mimarisi seçmenin önemini vurgular. DenseNet-121, doğru ve güvenilir sınıflandırma için sağlam bir seçenek olarak ortaya çıkıyor ve klinik teşhis ve tıbbi görüntüleme alanlarında potansiyel uygulamalar sunuyor. Sonuç olarak, çalışmamız, MRG’nin beyin tümörü teşhisi açısından önemini ve derin öğrenme algoritmalarının doğruluğu ve hassasiyeti artırmadaki potansiyelini vurgular. DenseNet-121’e dayalı yaklaşımımız, nöro görüntüleme alanında hastaların bakımını ve sonuçlarını iyileştirmeye katkıda bulunarak klinik teşhis ve tıbbi görüntüleme uygulamaları için umut vaat etmektedir.

Anahtar Kelimeler

Beyin tümörü, MRI taramaları, Yapay zeka, Bilgisayar destekli görüntü analizleri

Kaynakça

• Abd El Kader, I., Xu, G., Shuai, Z., Saminu, S., Javaid, I., & Salim Ahmad, I. (2021). Differential deep convolutional neural network model for brain tumor classification. Brain Sciences, 11(3), 352.
• Abdusalomov, A. B., Mukhiddinov, M., & Whangbo, T. K. (2023). Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging. Cancers, 15(16), 4172.
• Abiwinanda, N., Hanif, M., Hesaputra, S. T., Handayani, A., & Mengko, T. R. (2019). Brain tumor classification using convolutional neural network. World Congress on Medical Physics and Biomedical Engineering 2018: June 3-8, 2018, Prague, Czech Republic (Vol. 1), 183–189.
• Afshar, P., Plataniotis, K. N., & Mohammadi, A. (2019). Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1368–1372.
• Alentorn, A., Hoang-Xuan, K., & Mikkelsen, T. (2016). Presenting signs and symptoms in brain tumors. Handbook of Clinical Neurology, 134, 19–26.
• Ayadi, W., Elhamzi, W., Charfi, I., & Atri, M. (2021). Deep CNN for brain tumor classification. Neural Processing Letters, 53, 671–700.
• Baliga, S., Gandola, L., Timmermann, B., Gail, H., Padovani, L., Janssens, G. O., & Yock, T. I. (2021). Brain tumors: Medulloblastoma, ATRT, ependymoma. Pediatric Blood & Cancer, 68, e28395.
• Bilal, H. (2023). Computed Tomography (CT) Scanning: Principles and Applications.
• Black, P. M. (1991). Brain tumors. New England Journal of Medicine, 324(22), 1555–1564.
• Brain Tumor Classification (MRI). (n.d.). https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
• Brain Tumor MRI Dataset. (n.d.). https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset
• Bühring, U., Herrlinger, U., Krings, T., Thiex, R., Weller, M., & Küker, W. (2001). MRI features of primary central nervous system lymphomas at presentation. Neurology, 57(3), 393–396.
• Cano-Valdez, A. M., & Sevilla-Lizcano, D. B. (2021). Pathological classification of brain tumors. Principles of Neuro-Oncology: Brain & Skull Base, 75–105.
• Chandana, S. R., Movva, S., Arora, M., & Singh, T. (2008). Primary brain tumors in adults. American Family Physician, 77(10), 1423–1430.
• Chattopadhyay, A., & Maitra, M. (2022). MRI-based brain tumour image detection using CNN based deep learning method. Neuroscience Informatics, 2(4), 100060.
• Cheng. (2017). brain tumor dataset. https://doi.org/10.6084/m9.figshare.1512427.v5
• Cheng, J., Yang, W., Huang, M., Huang, W., Jiang, J., Zhou, Y., Yang, R., Zhao, J., Feng, Y., Feng, Q., & others. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PloS One, 11(6), e0157112.
• Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258.
• DeAngelis, L. M. (2001). Brain tumors. New England Journal of Medicine, 344(2), 114–123.
• Deepak, S., & Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345.
• Dulac, C., & Wagner, S. (2006). Genetic analysis of brain circuits underlying pheromone signaling. Annu. Rev. Genet., 40, 449–467.
• Finding extreme points in contours with OpenCV. (n.d.). https://pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/
• Gore, J. C., & others. (2003). Principles and practice of functional MRI of the human brain. The Journal of Clinical Investigation, 112(1), 4–9.
• Hayashida, Y., Hirai, T., Morishita, S., Kitajima, M., Murakami, R., Korogi, Y., Makino, K., Nakamura, H., Ikushima, I., Yamura, M., & others. (2006). Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. American Journal of Neuroradiology, 27(7), 1419–1425.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
• Herholz, K., Langen, K.-J., Schiepers, C., & Mountz, J. M. (2012). Brain tumors. Seminars in Nuclear Medicine, 42(6), 356–370.
• Hernández-Hernández, A., Reyes-Moreno, I., Gutiérrez-Aceves, A., Guerrero-Juárez, V., Santos-Zambrano, J., López-Mart’inez, M., Castro-Mart’inez, E., Cacho-D’iaz, B., Méndez-Padilla, J. A., & González-Aguilar, A. (2018). Primary tumors of the central nervous system. Clinical experience at a third level center. Revista de Investigación Cl{’i}nica, 70(4), 177–183.
• Hilton, D. A., & Hanemann, C. O. (2014). Schwannomas and their pathogenesis. Brain Pathology, 24(3), 205–220.
• Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
• Joshi, A. A., & Aziz, R. M. (2024). Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data. International Journal of Imaging Systems and Technology, 34(2), 1–16. https://doi.org/10.1002/ima.23007
• Kaplan, K., Kaya, Y., Kuncan, M., & Ertunç, H. M. (2020). Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Medical Hypotheses, 139(March). https://doi.org/10.1016/j.mehy.2020.109696
• Katti, G., Ara, S. A., & Shireen, A. (2011). Magnetic resonance imaging (MRI)–A review. International Journal of Dental Clinics, 3(1), 65–70.
• Keras Applications. (n.d.). https://keras.io/api/applications
• Komori, T. (2017). The 2016 WHO classification of tumours of the central nervous system: the major points of revision. Neurologia Medico-Chirurgica, 57(7), 301–311.
• Momin, A. A., Recinos, M. A., Cioffi, G., Patil, N., Soni, P., Almeida, J. P., Kruchko, C., Barnholtz-Sloan, J. S., Recinos, P. F., & Kshettry, V. R. (2021). Descriptive epidemiology of craniopharyngiomas in the United States. Pituitary, 24, 517–522.
• Neumann, H. P. H., Eggert, H. R., Weigel, K., Friedburg, H., Wiestler, O. D., & Schollmeyer, P. (1989). Hemangioblastomas of the central nervous system: a 10-year study with special reference to von Hippel-Lindau syndrome. Journal of Neurosurgery, 70(1), 24–30.
• Nibhoria, S., Tiwana, K. K., Phutela, R., Bajaj, A., & Chhabra, S. (2015). Histopathological spectrum of central nervous system tumors: A single centre study of 100 cases. International Journal of Scientific Study, 3(6), 130–134.
• Ostrom, Q. T., Gittleman, H., Fulop, J., Liu, M., Blanda, R., Kromer, C., Wolinsky, Y., Kruchko, C., & Barnholtz-Sloan, J. S. (2015). CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-Oncology, 17(suppl_4), iv1–iv62.
• Pawełczyk, T., Pawełczyk, A., & Rabe-Jabłońska, J. (2012). Before you diagnose a patient with a conversion disorder, perform a thorough general medical and neurological examination. Case study. Psychiatria Polska, 46(3), 483–492.
• Phelps, M. E., & Mazziotta, J. C. (1985). Positron emission tomography: human brain function and biochemistry. Science, 228(4701), 799–809.
• Raschka, S. (2014). An overview of general performance metrics of binary classifier systems. ArXiv Preprint ArXiv:1410.5330.
• Raza, A., Ayub, H., Khan, J. A., Ahmad, I., S. Salama, A., Daradkeh, Y. I., Javeed, D., Ur Rehman, A., & Hamam, H. (2022). A hybrid deep learning-based approach for brain tumor classification. Electronics, 11(7), 1146.
• Seetha, J., & Raja, S. S. (2018). Brain tumor classification using convolutional neural networks. Biomedical & Pharmacology Journal, 11(3), 1457.
• Senan, E. M., Jadhav, M. E., Rassem, T. H., Aljaloud, A. S., Mohammed, B. A., Al-Mekhlafi, Z. G., & others. (2022). Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning. Computational and Mathematical Methods in Medicine, 2022.
• Shankar, G. M., Balaj, L., Stott, S. L., Nahed, B., & Carter, B. S. (2017). Liquid biopsy for brain tumors. Expert Review of Molecular Diagnostics, 17(10), 943–947.
• Sheline, G. E. (1977). Radiation therapy of brain tumors. Cancer, 39(S2), 873–881.
• Siddiqui, M. F., Reza, A. W., & Kanesan, J. (2015). An automated and intelligent medical decision support system for brain MRI scans classification. PloS One, 10(8), e0135875.
• Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826.
• Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning, 6105–6114.
• Taylor, T., Dineen, R. A., Gardiner, D. C., Buss, C. H., Howatson, A., & Pace, N. L. (2014). Computed tomography (CT) angiography for confirmation of the clinical diagnosis of brain death. Cochrane Database of Systematic Reviews, 3.
• Thakor, N. V, & Tong, S. (2004). Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng., 6, 453–495.
• Theodros, D., Patel, M., Ruzevick, J., Lim, M., & Bettegowda, C. (2015). Pituitary adenomas: historical perspective, surgical management and future directions. CNS Oncology, 4(6), 411–429.

Analysis of Transfer Learning-Based Algorithms for Tumor Detection in Medical Imaging Data

Yıl 2024, Cilt: 14 Sayı: 3, 1322 – 1339, 15.09.2024

Cem Demirel , Emel Soylu

https://doi.org/10.31466/kfbd.1455542

Öz

Magnetic Resonance Imaging (MRI) has become a vital tool in the diagnosis of brain tumors due to its non-invasive nature and high-resolution imaging capabilities. In this study, we compared the performances of deep learning algorithms. A comprehensive dataset of MRI scans was utilized to train and validate our model, ensuring robust performance across various tumor types and imaging conditions. The results demonstrate the effectiveness of our approach, achieving a high level of accuracy and sensitivity in tumor detection. Our work contributes to the development of efficient and reliable tools for early diagnosis and monitoring of brain tumors, ultimately enhancing patient care and outcomes in the field of neuroimaging. Our findings highlight the significance of selecting an appropriate deep neural network architecture when dealing with brain MRI image classification tasks. DenseNet-121 emerges as a robust choice for accurate and reliable classification, offering potential applications in clinical diagnostics and medical imaging. In conclusion, our study underscores the importance of MRI in brain tumor diagnosis and the potential of deep learning algorithms to enhance accuracy and sensitivity. Our approach, based on DenseNet-121, holds promise for clinical diagnostics and medical imaging applications, contributing to improved patient care and outcomes in neuroimaging.

Anahtar Kelimeler

Brain tumor, MRI scans, Artificial intelligence, Computer-assisted image analyses

Kaynakça

• Abd El Kader, I., Xu, G., Shuai, Z., Saminu, S., Javaid, I., & Salim Ahmad, I. (2021). Differential deep convolutional neural network model for brain tumor classification. Brain Sciences, 11(3), 352.
• Abdusalomov, A. B., Mukhiddinov, M., & Whangbo, T. K. (2023). Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging. Cancers, 15(16), 4172.
• Abiwinanda, N., Hanif, M., Hesaputra, S. T., Handayani, A., & Mengko, T. R. (2019). Brain tumor classification using convolutional neural network. World Congress on Medical Physics and Biomedical Engineering 2018: June 3-8, 2018, Prague, Czech Republic (Vol. 1), 183–189.
• Afshar, P., Plataniotis, K. N., & Mohammadi, A. (2019). Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1368–1372.
• Alentorn, A., Hoang-Xuan, K., & Mikkelsen, T. (2016). Presenting signs and symptoms in brain tumors. Handbook of Clinical Neurology, 134, 19–26.
• Ayadi, W., Elhamzi, W., Charfi, I., & Atri, M. (2021). Deep CNN for brain tumor classification. Neural Processing Letters, 53, 671–700.
• Baliga, S., Gandola, L., Timmermann, B., Gail, H., Padovani, L., Janssens, G. O., & Yock, T. I. (2021). Brain tumors: Medulloblastoma, ATRT, ependymoma. Pediatric Blood & Cancer, 68, e28395.
• Bilal, H. (2023). Computed Tomography (CT) Scanning: Principles and Applications.
• Black, P. M. (1991). Brain tumors. New England Journal of Medicine, 324(22), 1555–1564.
• Brain Tumor Classification (MRI). (n.d.). https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
• Brain Tumor MRI Dataset. (n.d.). https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset
• Bühring, U., Herrlinger, U., Krings, T., Thiex, R., Weller, M., & Küker, W. (2001). MRI features of primary central nervous system lymphomas at presentation. Neurology, 57(3), 393–396.
• Cano-Valdez, A. M., & Sevilla-Lizcano, D. B. (2021). Pathological classification of brain tumors. Principles of Neuro-Oncology: Brain & Skull Base, 75–105.
• Chandana, S. R., Movva, S., Arora, M., & Singh, T. (2008). Primary brain tumors in adults. American Family Physician, 77(10), 1423–1430.
• Chattopadhyay, A., & Maitra, M. (2022). MRI-based brain tumour image detection using CNN based deep learning method. Neuroscience Informatics, 2(4), 100060.
• Cheng. (2017). brain tumor dataset. https://doi.org/10.6084/m9.figshare.1512427.v5
• Cheng, J., Yang, W., Huang, M., Huang, W., Jiang, J., Zhou, Y., Yang, R., Zhao, J., Feng, Y., Feng, Q., & others. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PloS One, 11(6), e0157112.
• Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258.
• DeAngelis, L. M. (2001). Brain tumors. New England Journal of Medicine, 344(2), 114–123.
• Deepak, S., & Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345.
• Dulac, C., & Wagner, S. (2006). Genetic analysis of brain circuits underlying pheromone signaling. Annu. Rev. Genet., 40, 449–467.
• Finding extreme points in contours with OpenCV. (n.d.). https://pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/
• Gore, J. C., & others. (2003). Principles and practice of functional MRI of the human brain. The Journal of Clinical Investigation, 112(1), 4–9.
• Hayashida, Y., Hirai, T., Morishita, S., Kitajima, M., Murakami, R., Korogi, Y., Makino, K., Nakamura, H., Ikushima, I., Yamura, M., & others. (2006). Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. American Journal of Neuroradiology, 27(7), 1419–1425.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
• Herholz, K., Langen, K.-J., Schiepers, C., & Mountz, J. M. (2012). Brain tumors. Seminars in Nuclear Medicine, 42(6), 356–370.
• Hernández-Hernández, A., Reyes-Moreno, I., Gutiérrez-Aceves, A., Guerrero-Juárez, V., Santos-Zambrano, J., López-Mart’inez, M., Castro-Mart’inez, E., Cacho-D’iaz, B., Méndez-Padilla, J. A., & González-Aguilar, A. (2018). Primary tumors of the central nervous system. Clinical experience at a third level center. Revista de Investigación Cl{’i}nica, 70(4), 177–183.
• Hilton, D. A., & Hanemann, C. O. (2014). Schwannomas and their pathogenesis. Brain Pathology, 24(3), 205–220.
• Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
• Joshi, A. A., & Aziz, R. M. (2024). Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data. International Journal of Imaging Systems and Technology, 34(2), 1–16. https://doi.org/10.1002/ima.23007
• Kaplan, K., Kaya, Y., Kuncan, M., & Ertunç, H. M. (2020). Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Medical Hypotheses, 139(March). https://doi.org/10.1016/j.mehy.2020.109696
• Katti, G., Ara, S. A., & Shireen, A. (2011). Magnetic resonance imaging (MRI)–A review. International Journal of Dental Clinics, 3(1), 65–70.
• Keras Applications. (n.d.). https://keras.io/api/applications
• Komori, T. (2017). The 2016 WHO classification of tumours of the central nervous system: the major points of revision. Neurologia Medico-Chirurgica, 57(7), 301–311.
• Momin, A. A., Recinos, M. A., Cioffi, G., Patil, N., Soni, P., Almeida, J. P., Kruchko, C., Barnholtz-Sloan, J. S., Recinos, P. F., & Kshettry, V. R. (2021). Descriptive epidemiology of craniopharyngiomas in the United States. Pituitary, 24, 517–522.
• Neumann, H. P. H., Eggert, H. R., Weigel, K., Friedburg, H., Wiestler, O. D., & Schollmeyer, P. (1989). Hemangioblastomas of the central nervous system: a 10-year study with special reference to von Hippel-Lindau syndrome. Journal of Neurosurgery, 70(1), 24–30.
• Nibhoria, S., Tiwana, K. K., Phutela, R., Bajaj, A., & Chhabra, S. (2015). Histopathological spectrum of central nervous system tumors: A single centre study of 100 cases. International Journal of Scientific Study, 3(6), 130–134.
• Ostrom, Q. T., Gittleman, H., Fulop, J., Liu, M., Blanda, R., Kromer, C., Wolinsky, Y., Kruchko, C., & Barnholtz-Sloan, J. S. (2015). CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-Oncology, 17(suppl_4), iv1–iv62.
• Pawełczyk, T., Pawełczyk, A., & Rabe-Jabłońska, J. (2012). Before you diagnose a patient with a conversion disorder, perform a thorough general medical and neurological examination. Case study. Psychiatria Polska, 46(3), 483–492.
• Phelps, M. E., & Mazziotta, J. C. (1985). Positron emission tomography: human brain function and biochemistry. Science, 228(4701), 799–809.
• Raschka, S. (2014). An overview of general performance metrics of binary classifier systems. ArXiv Preprint ArXiv:1410.5330.
• Raza, A., Ayub, H., Khan, J. A., Ahmad, I., S. Salama, A., Daradkeh, Y. I., Javeed, D., Ur Rehman, A., & Hamam, H. (2022). A hybrid deep learning-based approach for brain tumor classification. Electronics, 11(7), 1146.
• Seetha, J., & Raja, S. S. (2018). Brain tumor classification using convolutional neural networks. Biomedical & Pharmacology Journal, 11(3), 1457.
• Senan, E. M., Jadhav, M. E., Rassem, T. H., Aljaloud, A. S., Mohammed, B. A., Al-Mekhlafi, Z. G., & others. (2022). Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning. Computational and Mathematical Methods in Medicine, 2022.
• Shankar, G. M., Balaj, L., Stott, S. L., Nahed, B., & Carter, B. S. (2017). Liquid biopsy for brain tumors. Expert Review of Molecular Diagnostics, 17(10), 943–947.
• Sheline, G. E. (1977). Radiation therapy of brain tumors. Cancer, 39(S2), 873–881.
• Siddiqui, M. F., Reza, A. W., & Kanesan, J. (2015). An automated and intelligent medical decision support system for brain MRI scans classification. PloS One, 10(8), e0135875.
• Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826.
• Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning, 6105–6114.
• Taylor, T., Dineen, R. A., Gardiner, D. C., Buss, C. H., Howatson, A., & Pace, N. L. (2014). Computed tomography (CT) angiography for confirmation of the clinical diagnosis of brain death. Cochrane Database of Systematic Reviews, 3.
• Thakor, N. V, & Tong, S. (2004). Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng., 6, 453–495.
• Theodros, D., Patel, M., Ruzevick, J., Lim, M., & Bettegowda, C. (2015). Pituitary adenomas: historical perspective, surgical management and future directions. CNS Oncology, 4(6), 411–429.

Toplam 52 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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