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Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi

Yıl 2023, Cilt: 6 Sayı: 1, 75 - 84, 15.03.2023

Tuğrul Hakan Gençtürk Fidan Kaya Gülağız İsmail Kaya

https://doi.org/10.38016/jista.1215025

Öz

Teknolojinin gelişmesiyle birlikte yapay zekâ temelli uygulamalar pek çok alanda destek amaçlı kullanılmaktadır. Sağlık sektörü de bu tür uygulamaların yaygın kullanıldığı alanlardan bir tanesidir. Sağlık sektöründe teknolojik gelişime bağlı olarak meydana gelen bilgi artışı beraberinde radyolojik değerlendirmede uzmanlık gereğini doğurmuştur. Yoğun çalışma saatleri, sağlık kurumlarında her branştan uzmana ulaşılamaması ve özellikle acil patolojilerde erken teşhisin önemi göz önünde bulundurulduğunda hekimlere teşhis sürecinde destek olacak uygulamalara olan ihtiyacın önemi anlaşılmaktadır. Çalışma kapsamında Bilgisayarlı Tomografi (BT) görüntüleri kullanılarak beyin kanamalarının tespitini gerçekleştirmek amacıyla güncel derin öğrenme yöntemlerinden Görsel Geometri Grubu (VGG), Artık Sinir Ağı (ResNet) ve EfficientNet mimarileri yine güncel bir veri kümesi olan PhysioNet’e uygulanmıştır. Modeller doğruluk, kesinlik, hassasiyet ve F1 skor metrikleri kullanılarak hem kendi aralarında hem de literatürdeki çalışmalarla karşılaştırılmıştır. Gerçekleştirilen çalışma ile veri kümesine uygun model seçiminin önemi güncel modeller üzerinden ortaya konulmuştur. EfficientNet-B2 modelinin başarısı hem literatürdeki çalışmalardan hem de makale kapsamında değerlendirilen modellerden yüksek olmuştur. Elde edilen sonuçlar güncel derin öğrenme modellerinin, beyin kanaması teşhisine yardımcı olabilecek potansiyelde olduğunu göstermiştir. Çalışma acil servislerin yükünü çeken pratisyen hekimleri en azından beyin kanamasının varlığı konusunda uyarıp kanama durumunun gözden kaçmamasını sağlaması ve erken teşhisi açısından önem arz etmektedir.

Anahtar Kelimeler

Beyin kanaması BT Derin Öğrenme EfficientNet VGG ResNet

Kaynakça

  • Alawad, D. M., Mishra, A., Hoque, M. T., 2020. AIBH: accurate identification of brain hemorrhage using genetic algorithm based feature selection and stacking. Machine Learning and Knowledge Extraction, 2(2), 56-77. https://doi.org/10.3390/make2020005
  • AlOthman, A. F., Sait, A. R. W., Alhussain, T. A., 2022. Detecting coronary artery disease from computed tomography images using a deep learning technique. Diagnostics, 12(9), 2073. https://doi.org/10.3390/diagnostics12092073
  • Alquzi, S., Alhichri, H., Bazi, Y., 2021. Detection of COVID-19 using EfficientNet-B3 CNN and chest computed tomography images. ICICC 2021, International Conference on Innovative Computing and Communications, February 2021, Delhi, India, pp. 365-373.
  • Altuve, M., Pérez, A., 2022. Intracerebral hemorrhage detection on computed tomography images using a residual neural network. Physica Medica, 99, 113-119. https://doi.org/10.1016/j.ejmp.2022.05.015
  • Anupama, C. S. S., Sivaram, M., Lydia, E. L., Gupta, D., Shankar, K., 2022. Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Personal and Ubiquitous Computing, 26, 1-10. https://doi.org/10.1007/s00779-020-01492-2
  • Burduja, M., Ionescu, R. T., Verga, N., 2020. Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks. Sensors, 20(19), 5611. https://doi.org/10.3390/s20195611
  • Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., 2009. ImageNet: A large-scale hierarchical image database. CVPR09, IEEE Conference on Computer Vision and Pattern Recognition, 20-25 June 2009, Miami, Florida, USA, pp. 248-255.
  • Gautam, A., Raman, B., 2021. Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. Biomedical Signal Processing and Control, 63, 102178. https://doi.org/10.1016/j.bspc.2020.102178
  • Grewal, M., Srivastava, M. M., Kumar, P., Varadarajan, S., 2018. Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 4-7 April 2018, Washington, D.C, U.S., pp. 281-284.
  • He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. CVPR, IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June 2016, Las Vegas, Nevada, U. S., pp. 770-778.
  • Hssayeni, M., Croock, M. S., Salman, A. D., Al-khafaji, H. F., Yahya, Z. A., Ghoraani, B., 2020. Intracranial hemorrhage segmentation using a deep convolutional model. Data, 5(1). 14. https://doi.org/10.13026/4nae-zg36
  • Ko, H., Chung, H., Lee, H., Lee, J., 2020. Feasible study on intracranial hemorrhage detection and classification using a cnn-lstm network. EMBC, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 20-24 July 2020, Montreal, Canada, pp.1290-1293.
  • Kuno, H., Sekiya, K., Chapman, M. N., Sakai, O., 2017. Miscellaneous and emerging applications of dual-energy computed tomography for the evaluation of intracranial pathology. Neuroimaging Clinics, 27(3), 411-427. https://doi.org/10.1016/j.nic.2017.03.005
  • Lewick, T., Kumar, M., Hong, R., Wu, W., 2020. Intracranial hemorrhage detection in CT scans using deep learning. IEEE Sixth International Conference on Big Data Computing Service and Applications, 3-6 August 2020, Oxford, United Kingdom, pp.169-172.
  • Li, R., Xiao, C., Huang, Y., Hassan, H., Huang, B., 2022. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review. Diagnostics, 12(2), 298. https://doi.org/10.3390/diagnostics12020298
  • Liu, J., Wang, M., Bao, L., Li, X., 2020. EfficientNet based recognition of maize diseases by leaf image classification. Journal of Physics: Conference Series, 1693(1), 012148. https://doi.org/10.1088/1742-6596/1693/1/012148
  • Meng, F., Wang, J., Zhang, H., Li, W., 2022. Artificial intelligence-enabled medical analysis for intracranial cerebral hemorrhage detection and classification. Journal of Healthcare Engineering, 2022, 1-13. https://doi.org/10.1155/2022/2017223
  • Mirzai, H., Yağlı, N., Tekin, İ., 2005. Celal Bayar Üniversitesi Tıp Fakültesi acil birimine başvuran kafa travmalı olguların epidemiyolojik ve klinik özellikleri. Ulusal Travma Dergisi, 2, 146-152.
  • Morgan, N., Van Gerven, A., Smolders, A., de Faria Vasconcelos, K., Willems, H., Jacobs, R., 2022. Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images. Scientific Reports, 12(1), 1-9. https://doi.org/10.1038/s41598-022-11483-3
  • Mushtaq, M. F., Shahroz, M., Aseere, A. M., Shah, H., Majeed, R., Shehzad, D., Samad, A., 2021. BHCNet: neural network-based brain hemorrhage classification using head CT Scan. IEEE Access, 9, 113901-113916. https://doi.org/10.1109/ACCESS.2021.3102740
  • Phan A.-C., Nguyen T.-M.-N., Phan T.-C., 2019. Detection and classification of brain hemorrhage based on hounsfield values and convolution neural network technique. RIVF, 2019 IEEE-RIVF International Conference on Computing and Communication Technologies, 20-22 March 2019, Vietnam, pp.1-7.
  • Rahman, A. I., Bhuiyan, S., Reza, Z. H., Zaheen, J., Khan, T. A. N., Karim, D. Z., 2022. Intracranial hemorrhage detection on CT scan images using transfer learning approach of convolutional neural network. ICCA '22, 2nd International Conference on Computing Advancements, 10-12 March 2022, Dhaka Bangladesh, pp. 171-177.
  • Ravi, V., Narasimhan, H., Pham, T. D., 2021. EfficientNet-based convolutional neural networks for tuberculosis classification. In: Pham, T.D., Yan, H., Ashraf, M.W., Sjöberg, F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology, 31, 227-244. https://doi.org/10.1007/978-3-030-69951-2_9
  • Rim, B., Kim, J., Hong, M., 2020. Gender classification from fingerprint-images using deep learning approach. RACS '20, International conference on research in adaptive and convergent systems, 13-16 October 2020, Gwangju Republic of Korea, pp. 7-12.
  • Rogatsky, G., Mayevsky, A., Zarchin, N., Doron, A., 1996. Continuous multiparametric monitoring of brain activities following fluid-percussion injury in rats: preliminary results. Journal of basic and clinical physiology and pharmacology, 7(1), 23-44. https://doi.org/10.1515/jbcpp.1996.7.1.23
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. IEEE conference on computer vision and pattern recognition, 18-23 June 2018, Salt Lake City, UT, USA, pp. 4510-4520.
  • Simonyan, K., Zisserman, A. 2015. Very deep convolutional networks for large-scale image recognition. ICLR 2015, 3rd International Conference on Learning Representations, 7-9 May 2015, San Diego, CA, USA. https://doi.org/10.48550/arXiv.1409.1556
  • Solorio-Ramírez, J.-L., Saldana-Perez, M., Lytras, M. D., Moreno-Ibarra, M.-A., Yáñez-Márquez, C., 2021. Brain hemorrhage classification in CT scan images using minimalist machine learning. Diagnostics, 11(8), 1449. https://doi.org/10.3390/diagnostics11081449
  • T. C. Sağlık Bakanlığı, 2019. Sağlık İstatistikleri Yıllığı. https://dosyasb.saglik.gov.tr/Eklenti/40564,saglik-istatistikleri-yilligi-2019pdf.pdf?0
  • Tan, M., Le, Q., 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML 2019, The 36th International conference on machine learning, 10-15 June 2019, Long Beach, California, USA ,pp. 6105-6114.
  • TBI Data | Concussion | Traumatic Brain Injury | CDC Injury Center. 2022, Mart 21. https://www.cdc.gov/traumaticbraininjury/data/index.html
  • Toğaçar, M., Cömert, Z., Ergen, B., Budak, Ü., 2019. Brain hemorrhage detection based on heat maps, autoencoder and CNN architecture. UBMYK, 1st International Informatics and Software Engineering Conference, 6-7 November 2019, Ankara, Turkey, pp. 1-5.
  • Türkiye Ministry of Health Expert Board in Medicine. Türkiye emergency medicine specialty training curriculum. 2022, Mart 21. https://tuk.saglik.gov.tr/Eklenti/34065/0/aciltipmufredatv24doc.doc
  • Venugopal, D., Jayasankar, T., Sikkandar, M. Y., Waly, M. I., Pustokhina, I. V., Pustokhin, D. A., Shankar, K., 2021. A novel deep neural network for intracranial haemorrhage detection and classification. Computers, Materials & Continua, 68(3), 2877-2893. https://doi.org/10.32604/cmc.2021.015480
  • Wallis, A., McCoubrie, P., 2011. The radiology report—are we getting the message across?. Clinical radiology, 66(11), 1015-1022. https://doi.org/10.1016/j.crad.2011.05.013
  • Wang, P., Liu, J., Xu, L., Huang, P., Luo, X., Hu, Y., Kang, Z., 2021. Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism. Agriculture, 11(5), 393. https://doi.org/10.3390/agriculture11050393
  • Yalçın, S., Vural, H., 2022. Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks. Computers in Biology and Medicine, 149, 105941. https://doi.org/10.1016/j.compbiomed.2022.105941
  • Zeng, W., Li, G., Turbat, V., Hu, G., Ahn, H., Shen, J., 2021. Optimizing preventive medicine to bridge the gap between clinical medicine and public health for disease control in China: a lesson from COVID-19. Preventive Medicine, 143, 106324. https://doi.org/10.1016/j.ypmed.2020.106324
  • Zhang, M., Gu, S., Shi, Y., 2022. The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. Complex & Intelligent Systems, 8, 5545–5561. https://doi.org/10.1007/s40747-022-00724-7

A Comparative Analysis of Brain Hemorrhage Diagnosis on CT Scans Using Deep Learning Methods

Yıl 2023, Cilt: 6 Sayı: 1, 75 - 84, 15.03.2023

Tuğrul Hakan Gençtürk Fidan Kaya Gülağız İsmail Kaya

https://doi.org/10.38016/jista.1215025

Öz

With the development of technology, artificial intelligence-based applications are used for support in many areas. The health sector is one of the areas where such applications are widely used. The increase in knowledge in the health sector due to technological development has led to the need for expertise in radiological evaluation. Considering the intensive working hours, the inaccessibility of specialists from every branch in health institutions and the importance of early diagnosis, especially in emergency pathologies, the importance of the need for applications that will support physicians in the diagnosis process is understood. In the scope of the study, Visual Geometry Group (VGG), Residual Neural Network (ResNet) and EfficientNet architectures, which are among the current deep learning methods, were applied to PhysioNet, a recent dataset, in order to detect brain hemorrhages using Computed Tomography (CT) images. The models were compared among themselves and with existing studies in the literature using accuracy, precision, recall and F1 score metrics. With this study, the importance of choosing the appropriate model for the dataset has been demonstrated through current models. The success of the EfficientNet-B2 model was higher than both the studies in the literature and the models evaluated within the scope of the article. The results show that current deep-learning models have the potential to help in the diagnosis of an intracranial hemorrhage. The study is essential in terms of early diagnosis of intracranial hemorrhage by at least alerting general practitioners, who bear the burden of emergency services, to the presence of intracranial hemorrhage and ensuring that the bleeding condition is not overlooked.

Anahtar Kelimeler

Brain Hemorrhage CT Deep Learning EfficientNet ResNet VGG

Kaynakça

  • Alawad, D. M., Mishra, A., Hoque, M. T., 2020. AIBH: accurate identification of brain hemorrhage using genetic algorithm based feature selection and stacking. Machine Learning and Knowledge Extraction, 2(2), 56-77. https://doi.org/10.3390/make2020005
  • AlOthman, A. F., Sait, A. R. W., Alhussain, T. A., 2022. Detecting coronary artery disease from computed tomography images using a deep learning technique. Diagnostics, 12(9), 2073. https://doi.org/10.3390/diagnostics12092073
  • Alquzi, S., Alhichri, H., Bazi, Y., 2021. Detection of COVID-19 using EfficientNet-B3 CNN and chest computed tomography images. ICICC 2021, International Conference on Innovative Computing and Communications, February 2021, Delhi, India, pp. 365-373.
  • Altuve, M., Pérez, A., 2022. Intracerebral hemorrhage detection on computed tomography images using a residual neural network. Physica Medica, 99, 113-119. https://doi.org/10.1016/j.ejmp.2022.05.015
  • Anupama, C. S. S., Sivaram, M., Lydia, E. L., Gupta, D., Shankar, K., 2022. Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Personal and Ubiquitous Computing, 26, 1-10. https://doi.org/10.1007/s00779-020-01492-2
  • Burduja, M., Ionescu, R. T., Verga, N., 2020. Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks. Sensors, 20(19), 5611. https://doi.org/10.3390/s20195611
  • Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., 2009. ImageNet: A large-scale hierarchical image database. CVPR09, IEEE Conference on Computer Vision and Pattern Recognition, 20-25 June 2009, Miami, Florida, USA, pp. 248-255.
  • Gautam, A., Raman, B., 2021. Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. Biomedical Signal Processing and Control, 63, 102178. https://doi.org/10.1016/j.bspc.2020.102178
  • Grewal, M., Srivastava, M. M., Kumar, P., Varadarajan, S., 2018. Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 4-7 April 2018, Washington, D.C, U.S., pp. 281-284.
  • He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. CVPR, IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June 2016, Las Vegas, Nevada, U. S., pp. 770-778.
  • Hssayeni, M., Croock, M. S., Salman, A. D., Al-khafaji, H. F., Yahya, Z. A., Ghoraani, B., 2020. Intracranial hemorrhage segmentation using a deep convolutional model. Data, 5(1). 14. https://doi.org/10.13026/4nae-zg36
  • Ko, H., Chung, H., Lee, H., Lee, J., 2020. Feasible study on intracranial hemorrhage detection and classification using a cnn-lstm network. EMBC, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 20-24 July 2020, Montreal, Canada, pp.1290-1293.
  • Kuno, H., Sekiya, K., Chapman, M. N., Sakai, O., 2017. Miscellaneous and emerging applications of dual-energy computed tomography for the evaluation of intracranial pathology. Neuroimaging Clinics, 27(3), 411-427. https://doi.org/10.1016/j.nic.2017.03.005
  • Lewick, T., Kumar, M., Hong, R., Wu, W., 2020. Intracranial hemorrhage detection in CT scans using deep learning. IEEE Sixth International Conference on Big Data Computing Service and Applications, 3-6 August 2020, Oxford, United Kingdom, pp.169-172.
  • Li, R., Xiao, C., Huang, Y., Hassan, H., Huang, B., 2022. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review. Diagnostics, 12(2), 298. https://doi.org/10.3390/diagnostics12020298
  • Liu, J., Wang, M., Bao, L., Li, X., 2020. EfficientNet based recognition of maize diseases by leaf image classification. Journal of Physics: Conference Series, 1693(1), 012148. https://doi.org/10.1088/1742-6596/1693/1/012148
  • Meng, F., Wang, J., Zhang, H., Li, W., 2022. Artificial intelligence-enabled medical analysis for intracranial cerebral hemorrhage detection and classification. Journal of Healthcare Engineering, 2022, 1-13. https://doi.org/10.1155/2022/2017223
  • Mirzai, H., Yağlı, N., Tekin, İ., 2005. Celal Bayar Üniversitesi Tıp Fakültesi acil birimine başvuran kafa travmalı olguların epidemiyolojik ve klinik özellikleri. Ulusal Travma Dergisi, 2, 146-152.
  • Morgan, N., Van Gerven, A., Smolders, A., de Faria Vasconcelos, K., Willems, H., Jacobs, R., 2022. Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images. Scientific Reports, 12(1), 1-9. https://doi.org/10.1038/s41598-022-11483-3
  • Mushtaq, M. F., Shahroz, M., Aseere, A. M., Shah, H., Majeed, R., Shehzad, D., Samad, A., 2021. BHCNet: neural network-based brain hemorrhage classification using head CT Scan. IEEE Access, 9, 113901-113916. https://doi.org/10.1109/ACCESS.2021.3102740
  • Phan A.-C., Nguyen T.-M.-N., Phan T.-C., 2019. Detection and classification of brain hemorrhage based on hounsfield values and convolution neural network technique. RIVF, 2019 IEEE-RIVF International Conference on Computing and Communication Technologies, 20-22 March 2019, Vietnam, pp.1-7.
  • Rahman, A. I., Bhuiyan, S., Reza, Z. H., Zaheen, J., Khan, T. A. N., Karim, D. Z., 2022. Intracranial hemorrhage detection on CT scan images using transfer learning approach of convolutional neural network. ICCA '22, 2nd International Conference on Computing Advancements, 10-12 March 2022, Dhaka Bangladesh, pp. 171-177.
  • Ravi, V., Narasimhan, H., Pham, T. D., 2021. EfficientNet-based convolutional neural networks for tuberculosis classification. In: Pham, T.D., Yan, H., Ashraf, M.W., Sjöberg, F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology, 31, 227-244. https://doi.org/10.1007/978-3-030-69951-2_9
  • Rim, B., Kim, J., Hong, M., 2020. Gender classification from fingerprint-images using deep learning approach. RACS '20, International conference on research in adaptive and convergent systems, 13-16 October 2020, Gwangju Republic of Korea, pp. 7-12.
  • Rogatsky, G., Mayevsky, A., Zarchin, N., Doron, A., 1996. Continuous multiparametric monitoring of brain activities following fluid-percussion injury in rats: preliminary results. Journal of basic and clinical physiology and pharmacology, 7(1), 23-44. https://doi.org/10.1515/jbcpp.1996.7.1.23
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. IEEE conference on computer vision and pattern recognition, 18-23 June 2018, Salt Lake City, UT, USA, pp. 4510-4520.
  • Simonyan, K., Zisserman, A. 2015. Very deep convolutional networks for large-scale image recognition. ICLR 2015, 3rd International Conference on Learning Representations, 7-9 May 2015, San Diego, CA, USA. https://doi.org/10.48550/arXiv.1409.1556
  • Solorio-Ramírez, J.-L., Saldana-Perez, M., Lytras, M. D., Moreno-Ibarra, M.-A., Yáñez-Márquez, C., 2021. Brain hemorrhage classification in CT scan images using minimalist machine learning. Diagnostics, 11(8), 1449. https://doi.org/10.3390/diagnostics11081449
  • T. C. Sağlık Bakanlığı, 2019. Sağlık İstatistikleri Yıllığı. https://dosyasb.saglik.gov.tr/Eklenti/40564,saglik-istatistikleri-yilligi-2019pdf.pdf?0
  • Tan, M., Le, Q., 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML 2019, The 36th International conference on machine learning, 10-15 June 2019, Long Beach, California, USA ,pp. 6105-6114.
  • TBI Data | Concussion | Traumatic Brain Injury | CDC Injury Center. 2022, Mart 21. https://www.cdc.gov/traumaticbraininjury/data/index.html
  • Toğaçar, M., Cömert, Z., Ergen, B., Budak, Ü., 2019. Brain hemorrhage detection based on heat maps, autoencoder and CNN architecture. UBMYK, 1st International Informatics and Software Engineering Conference, 6-7 November 2019, Ankara, Turkey, pp. 1-5.
  • Türkiye Ministry of Health Expert Board in Medicine. Türkiye emergency medicine specialty training curriculum. 2022, Mart 21. https://tuk.saglik.gov.tr/Eklenti/34065/0/aciltipmufredatv24doc.doc
  • Venugopal, D., Jayasankar, T., Sikkandar, M. Y., Waly, M. I., Pustokhina, I. V., Pustokhin, D. A., Shankar, K., 2021. A novel deep neural network for intracranial haemorrhage detection and classification. Computers, Materials & Continua, 68(3), 2877-2893. https://doi.org/10.32604/cmc.2021.015480
  • Wallis, A., McCoubrie, P., 2011. The radiology report—are we getting the message across?. Clinical radiology, 66(11), 1015-1022. https://doi.org/10.1016/j.crad.2011.05.013
  • Wang, P., Liu, J., Xu, L., Huang, P., Luo, X., Hu, Y., Kang, Z., 2021. Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism. Agriculture, 11(5), 393. https://doi.org/10.3390/agriculture11050393
  • Yalçın, S., Vural, H., 2022. Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks. Computers in Biology and Medicine, 149, 105941. https://doi.org/10.1016/j.compbiomed.2022.105941
  • Zeng, W., Li, G., Turbat, V., Hu, G., Ahn, H., Shen, J., 2021. Optimizing preventive medicine to bridge the gap between clinical medicine and public health for disease control in China: a lesson from COVID-19. Preventive Medicine, 143, 106324. https://doi.org/10.1016/j.ypmed.2020.106324
  • Zhang, M., Gu, S., Shi, Y., 2022. The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. Complex & Intelligent Systems, 8, 5545–5561. https://doi.org/10.1007/s40747-022-00724-7
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka
Bölüm Araştırma Makalesi
Yazarlar

Tuğrul Hakan Gençtürk 0000-0002-2736-271X

Fidan Kaya Gülağız 0000-0003-3519-9278

İsmail Kaya 0000-0002-4128-5845

Erken Görünüm Tarihi 27 Aralık 2022
Yayımlanma Tarihi 15 Mart 2023
Gönderilme Tarihi 5 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 6 Sayı: 1

Kaynak Göster

APA Gençtürk, T. H., Kaya Gülağız, F., & Kaya, İ. (2023). Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi. Journal of Intelligent Systems: Theory and Applications, 6(1), 75-84. https://doi.org/10.38016/jista.1215025
AMA Gençtürk TH, Kaya Gülağız F, Kaya İ. Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi. jista. Mart 2023;6(1):75-84. doi:10.38016/jista.1215025
Chicago Gençtürk, Tuğrul Hakan, Fidan Kaya Gülağız, ve İsmail Kaya. “Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi”. Journal of Intelligent Systems: Theory and Applications 6, sy. 1 (Mart 2023): 75-84. https://doi.org/10.38016/jista.1215025.
EndNote Gençtürk TH, Kaya Gülağız F, Kaya İ (01 Mart 2023) Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi. Journal of Intelligent Systems: Theory and Applications 6 1 75–84.
IEEE T. H. Gençtürk, F. Kaya Gülağız, ve İ. Kaya, “Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi”, jista, c. 6, sy. 1, ss. 75–84, 2023, doi: 10.38016/jista.1215025.
ISNAD Gençtürk, Tuğrul Hakan vd. “Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi”. Journal of Intelligent Systems: Theory and Applications 6/1 (Mart 2023), 75-84. https://doi.org/10.38016/jista.1215025.
JAMA Gençtürk TH, Kaya Gülağız F, Kaya İ. Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi. jista. 2023;6:75–84.
MLA Gençtürk, Tuğrul Hakan vd. “Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi”. Journal of Intelligent Systems: Theory and Applications, c. 6, sy. 1, 2023, ss. 75-84, doi:10.38016/jista.1215025.
Vancouver Gençtürk TH, Kaya Gülağız F, Kaya İ. Derin Öğrenme Yöntemleri Kullanılarak BT Taramalarında Beyin Kanaması Teşhisinin Karşılaştırmalı Bir Analizi. jista. 2023;6(1):75-84.
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