Research Article
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Artificial Intelligence-based Cerebrovascular Disease Detection on Brain Computed Tomography Images

Year 2022, Issue: 41, 175 - 182, 30.11.2022
https://doi.org/10.31590/ejosat.1176648

Abstract

Cerebrovascular disease (CVD) causes paralysis and even mortality in humans due to blockage or bleeding of brain vessels. The early diagnosis of the CVD type by the specialist can avoid these casualties with a correct course of treatment. However, it is not always possible to recruit enough specialists in hospitals or emergency services. Therefore, in this study, an artificial intelligence (AI)-based clinical decision support system for CVD detection from brain computed tomography (CT) images is proposed to improve the diagnostic results and relieve the burden of specialists. The deep learning model, a subset of AI, was implemented through a two-step process in which CVD is first detected and then classified as ischemic or hemorrhagic. Moreover, the developed system is integrated into our custom-designed desktop application that offers a user-friendly interface for CVD diagnosis. Experimental results prove that our system has great potential to improve early diagnosis and treatment for specialists, which contributes to the recovery rate of patients.

Supporting Institution

TUBITAK (2209-B Industry-Oriented Undergraduate Research Projects Support Program)

Project Number

1139B412100453

Thanks

This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under the 2209-B Industry-Oriented Undergraduate Research Projects Support Program with project number 1139B412100453.

References

  • Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., . . . Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:.01164
  • Aydın, S., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Sequence-to-Sequence Video Captioning with Residual Connected Gated Recurrent Units. J Avrupa Bilim ve Teknoloji Dergisi(35), 380-386.
  • Balbay, Y., Gagnon-Arpin, I., Malhan, S., Öksüz, M. E., Sutherland, G., Dobrescu, A., . . . Habib, M. (2018). Modeling the burden of cardiovascular disease in Turkey. Anatolian Journal of Cardiology 20(4), 235.
  • Betül, U., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Resnet based Deep Gated Recurrent Unit for Image Captioning on Smartphone. J Avrupa Bilim ve Teknoloji Dergisi(35), 610-615.
  • Çaylı, Ö., Makav, B., Kılıç, V., & Onan, A. (2020). Mobile Application Based Automatic Caption Generation for Visually Impaired. Paper presented at the International Conference on Intelligent and Fuzzy Systems.
  • Chin, C.-L., Lin, B.-J., Wu, G.-R., Weng, T.-C., Yang, C.-S., Su, R.-C., & Pan, Y.-J. (2017). An automated early ischemic stroke detection system using CNN deep learning algorithm. Paper presented at the 2017 IEEE 8th International conference on awareness science and technology (iCAST).
  • Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Dayani, M. A., Fatehi, D., Rostamzadeh, O., & Rostamzadeh, A. (2017). Evaluation the sensitivity of diffusion and perfusion weighted imaging in therapeutic timing of stroke. Research Journal of Pharmacy Technology, 10(6), 1951-1956.
  • Diaz, A. B. F., Belen, A. A., Tenorio-Javier, A. M. J., & Juangco, D. N. A. (2022). Cerebrovascular Disease in Asia: Causative Factors. In Hypertension and Cardiovascular Disease in Asia (pp. 271-284): Springer.
  • Dodge, S., & Karam, L. (2016). Understanding how image quality affects deep neural networks. Paper presented at the 2016 eighth international conference on quality of multimedia experience (QoMEX).
  • Doğan, V., Isik, T., Kilic, V., & Horzum, N. (2022). A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. Analytical Methods 14(35), 3458-3466.
  • Doğan, V., & Kılıç, V. (2021). Akıllı Telefon Kullanarak Yapay Zeka Tabanlı Farenjit Tespiti: Artificial Intelligence Based Pharyngitis Detection Using Smartphone. J Sağlık Bilimlerinde Yapay Zeka Dergisi, 1(2), 14-19.
  • Doğan, V., Yüzer, E., Kılıç, V., & Şen, M. (2021). Non-enzymatic colorimetric detection of hydrogen peroxide using a μPAD coupled with a machine learning-based smartphone app. Analyst 146(23), 7336-7344.
  • Erkoyun, E., Sözmen, K., Bennett, K., Unal, B., & Boshuizen, H. (2016). Predicting the health impact of lowering salt consumption in Turkey using the DYNAMO health impact assessment tool. J Public Health, 140, 228-234.
  • Fetiler, B., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). Video captioning based on multi-layer gated recurrent unit for smartphones. J Avrupa Bilim ve Teknoloji Dergisi(32), 221-226.
  • Gölcez, T., Kiliç, V., & Şen, M. (2021). A portable smartphone-based platform with an offline image-processing tool for the rapid paper-based colorimetric detection of glucose in artificial saliva. Analytical Sciences 37(4), 561-567.
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., . . . Cai, J. (2018). Recent advances in convolutional neural networks. Pattern recognition 77, 354-377.
  • Hsieh, Y.-Z., Luo, Y.-C., Pan, C., Su, M.-C., Chen, C.-J., & Hsieh, K. L.-C. (2019). Cerebral small vessel disease biomarkers detection on MRI-sensor-based image and deep learning. Sensors 19(11), 2573.
  • Jeon, C. H., Park, J. S., Lee, J. H., Kim, H., Kim, S. C., Park, K. H., . . . Kim, Y.-M. (2017). Comparison of brain computed tomography and diffusion-weighted magnetic resonance imaging to predict early neurologic outcome before target temperature management comatose cardiac arrest survivors. Resuscitation 118, 21-26.
  • Jo, J., & Jadidi, Z. (2020). A high precision crack classification system using multi-layered image processing and deep belief learning. Structure Infrastructure Engineering, 16(2), 297-305.
  • Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. Paper presented at the European conference on computer vision.
  • Katti, G., Ara, S. A., & Shireen, A. (2011). Magnetic resonance imaging (MRI)–A review. International journal of dental clinics 3(1), 65-70.
  • Keskin, R., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). A benchmark for feature-injection architectures in image captioning. J Avrupa Bilim ve Teknoloji Dergisi (31), 461-468.
  • Keskin, R., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). Multi-GRU based automated image captioning for smartphones. Paper presented at the 2021 29th Signal Processing and Communications Applications Conference (SIU).
  • Kilic, B., Dogan, V., Kilic, V., & Kahyaoglu, L. N. (2022). Colorimetric food spoilage monitoring with carbon dot and UV light reinforced fish gelatin films using a smartphone application. International Journal of Biological Macromolecules 209, 1562-1572.
  • Kılıç, V. (2021). Deep gated recurrent unit for smartphone-based image captioning. J Sakarya University Journal of Computer Information Sciences, 4(2), 181-191.
  • Kılıç, V., Barnard, M., Wang, W., & Kittler, J. (2013). Adaptive particle filtering approach to audio-visual tracking. Paper presented at the 21st European Signal Processing Conference (EUSIPCO 2013).
  • Kılıç, V., Mercan, Ö. B., Tetik, M., Kap, Ö., & Horzum, N. (2022). Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning. Analytical Sciences 38(2), 347-358.
  • Koç, U., Sezer, E. A., Özkaya, Y. A., Yarbay, Y., Taydaş, O., Ayyıldız, V. A., . . . Beşler, M. S. (2022). Artificial Intelligence in Healthcare Competition (Teknofest-2021): Stroke Data Set. The Eurasian Journal of Medicine.
  • Kökten, A., & Kılıç, V. (2021). Detection of COVID-19 Cases with Fuzzy Classifiers Using Chest Computed Tomography. J Avrupa Bilim ve Teknoloji Dergisi (26), 68-72.
  • Lewick, T., Kumar, M., Hong, R., & Wu, W. (2020). Intracranial hemorrhage detection in CT scans using deep learning. Paper presented at the 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService).
  • Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep learning for generic object detection: A survey. International journal of computer vision 128(2), 261-318.
  • Livne, M., Rieger, J., Aydin, O. U., Taha, A. A., Akay, E. M., Kossen, T., . . . Frey, D. (2019). A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Frontiers in neuroscience 13, 97.
  • Maharana, K., Mondal, S., & Nemade, B. (2022). A Review: Data Pre-Processing and Data Augmentation Techniques. Global Transitions Proceedings.
  • Mercan, Ö. B., Doğan, V., & Kılıç, V. (2020). Time Series Analysis based Machine Learning Classification for Blood Sugar Levels. Paper presented at the 2020 Medical Technologies Congress (TIPTEKNO).
  • Mercan, Ö. B., & Kılıç, V. (2020). Fuzzy classifier based colorimetric quantification using a smartphone. Paper presented at the International Conference on Intelligent and Fuzzy Systems.
  • Mercan, Ö. B., Kılıç, V., & Şen, M. (2021). Machine learning-based colorimetric determination of glucose in artificial saliva with different reagents using a smartphone coupled μPAD. Sensors Actuators B: Chemical 329, 129037.
  • Palaz, Z., Doğan, V., & Kılıç, V. (2021). Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks. J Avrupa Bilim ve Teknoloji Dergisi(32), 1168-1174.
  • Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:.04621
  • Rehman, A., Iqbal, M. A., Xing, H., & Ahmed, I. (2021). COVID-19 detection empowered with machine learning and deep learning techniques: A systematic review. Applied Sciences 11(8), 3414.
  • Şen, M., Yüzer, E., Doğan, V., Avcı, İ., Ensarioğlu, K., Aykaç, A., . . . Kılıç, V. (2022). Colorimetric detection of H2O2 with Fe3O4@ Chi nanozyme modified µPADs using artificial intelligence. Microchimica Acta 189(10), 1-11.
  • Sewak, M., Sahay, S. K., & Rathore, H. (2020). An overview of deep learning architecture of deep neural networks and autoencoders. Journal of Computational Theoretical Nanoscience 17(1), 182-188.
  • Sun, X., Qian, H., Xiong, Y., Zhu, Y., Huang, Z., & Yang, F. (2022). Deep learning-enabled mobile application for efficient and robust herb image recognition. Scientific Reports 12(1), 1-18.
  • Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
  • Tai, Y., Yang, J., & Liu, X. (2017). Image super-resolution via deep recursive residual network. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
  • Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Convolutional neural networks for multi-class brain disease detection using MRI images. Computerized Medical Imaging Graphics 78, 101673.
  • Taylor, L., & Nitschke, G. (2018). Improving deep learning with generic data augmentation. Paper presented at the 2018 IEEE Symposium Series on Computational Intelligence (SSCI).
  • Ullah, Z., Farooq, M. U., Lee, S.-H., & An, D. (2020). A hybrid image enhancement based brain MRI images classification technique. Medical hypotheses 143, 109922.
  • Yüzer, E., Doğan, V., Kılıç, V., & Şen, M. (2022). Smartphone embedded deep learning approach for highly accurate and automated colorimetric lactate analysis in sweat. Sensors Actuators B: Chemical 132489.
  • Zhao, C., Carass, A., Lee, J., He, Y., & Prince, J. L. (2017). Whole brain segmentation and labeling from CT using synthetic MR images. Paper presented at the International Workshop on Machine Learning in Medical Imaging.

Beyin Bilgisayarlı Tomografi Görüntülerinde Yapay Zeka Tabanlı Beyin Damar Hastalıkları Tespiti

Year 2022, Issue: 41, 175 - 182, 30.11.2022
https://doi.org/10.31590/ejosat.1176648

Abstract

Serebrovasküler hastalık (SVH), beyin damarlarının tıkanması veya kanaması nedeniyle insanlarda felce ve hatta ölüme neden olmaktadır. SVH tipinin uzman tarafından erken teşhisiyle olumsuz etkiler doğru bir tedavi süreci ile engellenebilir. Ancak, hastanelerde veya acil servislerde yeterli sayıda uzmanın görevlendirilmesi her zaman mümkün olmamaktadır. Bu nedenle, bu çalışmada, tanı sürecini hızlandırmak ve uzmanların yükünü hafifletmek için beyin bilgisayarlı tomografi görüntülerinden SVH tespiti için yapay zeka tabanlı bir klinik karar destek sistemi önerilmiştir. Yapay zekanın bir alt kümesi olan derin ögrenme modeli, SVH’nin önce tespit edildiği ve ardından iskemik veya hemorajik olarak sınıflandırıldığı iki aşamalı bir süreçle uygulanmıştır. Ayrıca geliştirilen sistem, SVH teşhisi için kullanıcı dostu bir arayüz sunan özel olarak tasarlanmış¸ masaüstü uygulamamıza entegre edilmiştir. Deneysel sonuçlar, sistemimizin uzmanlar için erken teşhis ve tedaviyi geliştirme konusunda büyük bir potansiyele sahip olduğunu ve hastaların iyileşme oranına katkıda bulunacağını göstermektedir.

Project Number

1139B412100453

References

  • Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., . . . Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:.01164
  • Aydın, S., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Sequence-to-Sequence Video Captioning with Residual Connected Gated Recurrent Units. J Avrupa Bilim ve Teknoloji Dergisi(35), 380-386.
  • Balbay, Y., Gagnon-Arpin, I., Malhan, S., Öksüz, M. E., Sutherland, G., Dobrescu, A., . . . Habib, M. (2018). Modeling the burden of cardiovascular disease in Turkey. Anatolian Journal of Cardiology 20(4), 235.
  • Betül, U., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Resnet based Deep Gated Recurrent Unit for Image Captioning on Smartphone. J Avrupa Bilim ve Teknoloji Dergisi(35), 610-615.
  • Çaylı, Ö., Makav, B., Kılıç, V., & Onan, A. (2020). Mobile Application Based Automatic Caption Generation for Visually Impaired. Paper presented at the International Conference on Intelligent and Fuzzy Systems.
  • Chin, C.-L., Lin, B.-J., Wu, G.-R., Weng, T.-C., Yang, C.-S., Su, R.-C., & Pan, Y.-J. (2017). An automated early ischemic stroke detection system using CNN deep learning algorithm. Paper presented at the 2017 IEEE 8th International conference on awareness science and technology (iCAST).
  • Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Dayani, M. A., Fatehi, D., Rostamzadeh, O., & Rostamzadeh, A. (2017). Evaluation the sensitivity of diffusion and perfusion weighted imaging in therapeutic timing of stroke. Research Journal of Pharmacy Technology, 10(6), 1951-1956.
  • Diaz, A. B. F., Belen, A. A., Tenorio-Javier, A. M. J., & Juangco, D. N. A. (2022). Cerebrovascular Disease in Asia: Causative Factors. In Hypertension and Cardiovascular Disease in Asia (pp. 271-284): Springer.
  • Dodge, S., & Karam, L. (2016). Understanding how image quality affects deep neural networks. Paper presented at the 2016 eighth international conference on quality of multimedia experience (QoMEX).
  • Doğan, V., Isik, T., Kilic, V., & Horzum, N. (2022). A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. Analytical Methods 14(35), 3458-3466.
  • Doğan, V., & Kılıç, V. (2021). Akıllı Telefon Kullanarak Yapay Zeka Tabanlı Farenjit Tespiti: Artificial Intelligence Based Pharyngitis Detection Using Smartphone. J Sağlık Bilimlerinde Yapay Zeka Dergisi, 1(2), 14-19.
  • Doğan, V., Yüzer, E., Kılıç, V., & Şen, M. (2021). Non-enzymatic colorimetric detection of hydrogen peroxide using a μPAD coupled with a machine learning-based smartphone app. Analyst 146(23), 7336-7344.
  • Erkoyun, E., Sözmen, K., Bennett, K., Unal, B., & Boshuizen, H. (2016). Predicting the health impact of lowering salt consumption in Turkey using the DYNAMO health impact assessment tool. J Public Health, 140, 228-234.
  • Fetiler, B., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). Video captioning based on multi-layer gated recurrent unit for smartphones. J Avrupa Bilim ve Teknoloji Dergisi(32), 221-226.
  • Gölcez, T., Kiliç, V., & Şen, M. (2021). A portable smartphone-based platform with an offline image-processing tool for the rapid paper-based colorimetric detection of glucose in artificial saliva. Analytical Sciences 37(4), 561-567.
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., . . . Cai, J. (2018). Recent advances in convolutional neural networks. Pattern recognition 77, 354-377.
  • Hsieh, Y.-Z., Luo, Y.-C., Pan, C., Su, M.-C., Chen, C.-J., & Hsieh, K. L.-C. (2019). Cerebral small vessel disease biomarkers detection on MRI-sensor-based image and deep learning. Sensors 19(11), 2573.
  • Jeon, C. H., Park, J. S., Lee, J. H., Kim, H., Kim, S. C., Park, K. H., . . . Kim, Y.-M. (2017). Comparison of brain computed tomography and diffusion-weighted magnetic resonance imaging to predict early neurologic outcome before target temperature management comatose cardiac arrest survivors. Resuscitation 118, 21-26.
  • Jo, J., & Jadidi, Z. (2020). A high precision crack classification system using multi-layered image processing and deep belief learning. Structure Infrastructure Engineering, 16(2), 297-305.
  • Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. Paper presented at the European conference on computer vision.
  • Katti, G., Ara, S. A., & Shireen, A. (2011). Magnetic resonance imaging (MRI)–A review. International journal of dental clinics 3(1), 65-70.
  • Keskin, R., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). A benchmark for feature-injection architectures in image captioning. J Avrupa Bilim ve Teknoloji Dergisi (31), 461-468.
  • Keskin, R., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). Multi-GRU based automated image captioning for smartphones. Paper presented at the 2021 29th Signal Processing and Communications Applications Conference (SIU).
  • Kilic, B., Dogan, V., Kilic, V., & Kahyaoglu, L. N. (2022). Colorimetric food spoilage monitoring with carbon dot and UV light reinforced fish gelatin films using a smartphone application. International Journal of Biological Macromolecules 209, 1562-1572.
  • Kılıç, V. (2021). Deep gated recurrent unit for smartphone-based image captioning. J Sakarya University Journal of Computer Information Sciences, 4(2), 181-191.
  • Kılıç, V., Barnard, M., Wang, W., & Kittler, J. (2013). Adaptive particle filtering approach to audio-visual tracking. Paper presented at the 21st European Signal Processing Conference (EUSIPCO 2013).
  • Kılıç, V., Mercan, Ö. B., Tetik, M., Kap, Ö., & Horzum, N. (2022). Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning. Analytical Sciences 38(2), 347-358.
  • Koç, U., Sezer, E. A., Özkaya, Y. A., Yarbay, Y., Taydaş, O., Ayyıldız, V. A., . . . Beşler, M. S. (2022). Artificial Intelligence in Healthcare Competition (Teknofest-2021): Stroke Data Set. The Eurasian Journal of Medicine.
  • Kökten, A., & Kılıç, V. (2021). Detection of COVID-19 Cases with Fuzzy Classifiers Using Chest Computed Tomography. J Avrupa Bilim ve Teknoloji Dergisi (26), 68-72.
  • Lewick, T., Kumar, M., Hong, R., & Wu, W. (2020). Intracranial hemorrhage detection in CT scans using deep learning. Paper presented at the 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService).
  • Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep learning for generic object detection: A survey. International journal of computer vision 128(2), 261-318.
  • Livne, M., Rieger, J., Aydin, O. U., Taha, A. A., Akay, E. M., Kossen, T., . . . Frey, D. (2019). A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Frontiers in neuroscience 13, 97.
  • Maharana, K., Mondal, S., & Nemade, B. (2022). A Review: Data Pre-Processing and Data Augmentation Techniques. Global Transitions Proceedings.
  • Mercan, Ö. B., Doğan, V., & Kılıç, V. (2020). Time Series Analysis based Machine Learning Classification for Blood Sugar Levels. Paper presented at the 2020 Medical Technologies Congress (TIPTEKNO).
  • Mercan, Ö. B., & Kılıç, V. (2020). Fuzzy classifier based colorimetric quantification using a smartphone. Paper presented at the International Conference on Intelligent and Fuzzy Systems.
  • Mercan, Ö. B., Kılıç, V., & Şen, M. (2021). Machine learning-based colorimetric determination of glucose in artificial saliva with different reagents using a smartphone coupled μPAD. Sensors Actuators B: Chemical 329, 129037.
  • Palaz, Z., Doğan, V., & Kılıç, V. (2021). Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks. J Avrupa Bilim ve Teknoloji Dergisi(32), 1168-1174.
  • Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:.04621
  • Rehman, A., Iqbal, M. A., Xing, H., & Ahmed, I. (2021). COVID-19 detection empowered with machine learning and deep learning techniques: A systematic review. Applied Sciences 11(8), 3414.
  • Şen, M., Yüzer, E., Doğan, V., Avcı, İ., Ensarioğlu, K., Aykaç, A., . . . Kılıç, V. (2022). Colorimetric detection of H2O2 with Fe3O4@ Chi nanozyme modified µPADs using artificial intelligence. Microchimica Acta 189(10), 1-11.
  • Sewak, M., Sahay, S. K., & Rathore, H. (2020). An overview of deep learning architecture of deep neural networks and autoencoders. Journal of Computational Theoretical Nanoscience 17(1), 182-188.
  • Sun, X., Qian, H., Xiong, Y., Zhu, Y., Huang, Z., & Yang, F. (2022). Deep learning-enabled mobile application for efficient and robust herb image recognition. Scientific Reports 12(1), 1-18.
  • Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
  • Tai, Y., Yang, J., & Liu, X. (2017). Image super-resolution via deep recursive residual network. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
  • Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Convolutional neural networks for multi-class brain disease detection using MRI images. Computerized Medical Imaging Graphics 78, 101673.
  • Taylor, L., & Nitschke, G. (2018). Improving deep learning with generic data augmentation. Paper presented at the 2018 IEEE Symposium Series on Computational Intelligence (SSCI).
  • Ullah, Z., Farooq, M. U., Lee, S.-H., & An, D. (2020). A hybrid image enhancement based brain MRI images classification technique. Medical hypotheses 143, 109922.
  • Yüzer, E., Doğan, V., Kılıç, V., & Şen, M. (2022). Smartphone embedded deep learning approach for highly accurate and automated colorimetric lactate analysis in sweat. Sensors Actuators B: Chemical 132489.
  • Zhao, C., Carass, A., Lee, J., He, Y., & Prince, J. L. (2017). Whole brain segmentation and labeling from CT using synthetic MR images. Paper presented at the International Workshop on Machine Learning in Medical Imaging.
There are 50 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ali Fatih Karataş 0000-0003-1872-7550

Vakkas Doğan 0000-0001-5934-4156

Volkan Kılıç 0000-0002-3164-1981

Project Number 1139B412100453
Early Pub Date October 2, 2022
Publication Date November 30, 2022
Published in Issue Year 2022 Issue: 41

Cite

APA Karataş, A. F., Doğan, V., & Kılıç, V. (2022). Artificial Intelligence-based Cerebrovascular Disease Detection on Brain Computed Tomography Images. Avrupa Bilim Ve Teknoloji Dergisi(41), 175-182. https://doi.org/10.31590/ejosat.1176648