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Derin Öğrenme ile Alzheimer Hastalığı Teşhisi İçin Model Önerisi

Yıl 2022, Sayı: 37, 123 - 130, 15.07.2022
https://doi.org/10.31590/ejosat.1136855

Öz

Alzheimer hastalığı çağın en büyük sağlık problemlerinden biridir. Bir tedavisi bulunmaması nedeniyle hastalığın erken evrelerde teşhis edilmesi ve önleyici tedavilerin uygulanması gerekmektedir. Ancak hastalığın erken teşhisi oldukça zordur, bu nedenle çoğu kişide belirgin ve geri dönüşsüz etkiler oluştuktan sonra teşhis yapılabilmektedir. Hastalığın erken teşhis edilmesi için dünyada araştırmacılar tarafından çeşitli çalışmalar yapılmaktadır. Deep learning, Alzheimer hastalığının erken teşhisinde son zamanlarda oldukça önem kazanmıştır. Deep learning ile oluşturulmuş modellerin kullanılmasıyla erken teşhis yapılabilme başarısı yüksek seviyelere ulaşmıştır. Bu çalışmada Alzheimer hastalığının oluşum evreleri ve oluşan değişiklikler incelenmiştir. Alzheimer’s teşhisinde kullanılan çeşitli teknikler için literatür taraması yapılmış ve görüntüleme tekniklerinin Alzheimer’s erken teşhisinde kullanımı araştırılmıştır.

Yaygın kullanımı nedeniyle MRI tekniği üzerinde durulmuş, çoğunlukla MRI kullanılan çalışmalar incelenmiştir. Deep learning’te kullanılan kavramlar açıklanmış, yenilikler ve sonuçlar ortaya konmuştur. Deep learning’te kullanılan mimariler ve bu alanda getirdikleri yenilikler ortaya konmuş, mevcut çalışmalarda oluşturulmuş ve test edilmiş deep learning modelleri incelenmiştir. Yapılan çeşitli çalışmaların getirdiği yenilikler ve başarı oranları ortaya konmuştur. Kullanım kolaylığı sağlayan ve hızlı, performanslı ve başaırılı bir model geliştirilmesi için çalışılmıştır. Bunun için scheduler yapısı, MONAI yapısı, “Data loader” yapısı ve çeşitli teknikler basit bir kullanımla sunulmuştur. Ayrıca model Google Colab üzerinde sorunsuz şekilde çalışması için optimize edilmiştir. Ayrıca görüntü önişlemede oldukça önemli olan FSL kütüphanesindeki toollar ile çalışılmış ve "Bias field and Neck Clean Up", “Standard Brain Extraction Using BET2” ve "Robust Brain Center Estimation" toolları için optimal parametreler bulunmuştur. Bu kütüphane ile herhangi bir model için optimal beyin görüntüleri elde edilebilmektedir. Modelde temel olarak DenseNet121 modeli kullanılmıştır ve kolaylıkla model değiştirilebilen bir yapıda sunulmuştur. Model 3 boyutlu MR görüntülerini doğrudan kullanabilmektedir ve bu sayede çeşitli uzaysal bilginin kaybının önüne geçilmiştir.

Destekleyen Kurum

İstanbul Teknik Üniversitesi

Kaynakça

  • Wee, C. Y., Yap, P. T., & Shen, D. (2012). Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping, 34(12), 3411–3425. https://doi.org/10.1002/hbm.22156
  • Bain LJ, Jedrziewski K, Morrison-Bogorad M, Albert M, Cotman C, Hendrie H, Trojanowski JQ (2008): Healthy brain aging: A meeting report from the Sylvan M. Cohen Annual Retreat of The University of Pennsylvania Institute On Aging. Alzheimers Dement 4:443–446.
  • Grundman M, Petersen RC, Ferris SH, Thomas RG, Aisen PS, Bennett DA, et al. (2004): Mild cognitive impairment can be distinguished from Alzheimer’s disease and normal aging for clinical trials. Arch Neurol 61:59–66.
  • Misra C, Fan Y, Davatzikos C (2009): Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI. Neuroimage 44:1414–1422.
  • Sarraf, S., & Tofighi, G. (2016). Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. 2016 Future Technologies Conference (FTC). https://doi.org/10.1109/ftc.2016.7821697
  • Kam, T. E., Zhang, H., & Shen, D. (2018). A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 293–301. https://doi.org/10.1007/978-3-030-00931-1_34
  • Yan, W., Zhang, H., Sui, J., & Shen, D. (2018). Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 249–257. https://doi.org/10.1007/978-3-030-00931-1_29
  • Dyrba, M., Barkhof, F., Fellgiebel, A., Filippi, M., Hausner, L., Hauenstein, K., Kirste, T., & Teipel, S. J. (2015). Predicting Prodromal Alzheimer’s Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of Neuroimaging, 25(5), 738–747. https://doi.org/10.1111/jon.12214
  • Zhang, Y., Teng, Q., Liu, Y., Liu, Y., & He, X. (2022). Diagnosis of Alzheimer’s disease based on regional attention with sMRI gray matter slices. Journal of Neuroscience Methods, 365, 109376. https://doi.org/10.1016/j.jneumeth.2021.109376
  • Li, W., Lin, X., & Chen, X. (2020). Detecting Alzheimer’s disease Based on 4D fMRI: An exploration under deep learning framework. Neurocomputing, 388, 280–287. https://doi.org/10.1016/j.neucom.2020.01.053
  • Ding, Y., Sohn, J. H., Kawczynski, M. G., Trivedi, H., Harnish, R., Jenkins, N. W., Lituiev, D., Copeland, T. P., Aboian, M. S., Mari Aparici, C., Behr, S. C., Flavell, R. R., Huang, S. Y., Zalocusky, K. A., Nardo, L., Seo, Y., Hawkins, R. A., Hernandez Pampaloni, M., Hadley, D., & Franc, B. L. (2019). A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology, 290(2), 456–464. https://doi.org/10.1148/radiol.2018180958
  • Dyrba, M., Ewers, M., Wegrzyn, M., Kilimann, I., Plant, C., Oswald, A., Meindl, T., Pievani, M., Bokde, A. L. W., Fellgiebel, A., Filippi, M., Hampel, H., Klöppel, S., Hauenstein, K., Kirste, T., & Teipel, S. J. (2013). Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer’s Disease Using Machine Learning Classification of Multicenter DTI Data. PLoS ONE, 8(5), e64925. https://doi.org/10.1371/journal.pone.0064925
  • Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X., & Zhao, X. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing, 333, 145–156. https://doi.org/10.1016/j.neucom.2018.12.018
  • Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., Maqsood, M., & Yaqub, M. (2021). A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images. Neuroscience, 460, 43–52. https://doi.org/10.1016/j.neuroscience.2021.01.002
  • ADNI | Alzheimer’s Disease Neuroimaging Initiative. (n.d.). Alzheimer’s Disease Neuroimaging Initiative. Retrieved January 18, 2022, from https://adni.loni.usc.edu/
  • ADNI | Alzheimer’s Disease Neuroimaging Initiative. (n.d.). Alzheimer’s Disease Neuroimaging Initiative. Retrieved January 18, 2022, from https://ida.loni.usc.edu/home/projectPage.jsp?project=ADNI&page=HOME&subPage=OVERVIEW_PR
  • FSL - FslWiki. (n.d.). FMRIB Software Library. Retrieved March 23, 2022, from https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL
  • M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86, 2009
  • S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004
  • S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002.
  • M. Jenkinson, M. Pechaud, and S. Smith. BET2: MR-based estimation of brain, skull and scalp surfaces. In Eleventh Annual Meeting of the Organization for Human Brain Mapping, 2005.
  • Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An Introductory Review of Deep Learning for Prediction Models With Big Data. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00004
  • Scherer, D., Müller, A., & Behnke, S. (2010). Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. Artificial Neural Networks – ICANN 2010, 92–101. https://doi.org/10.1007/978-3-642-15825-4_10
  • Le Cun, Y., Jackel, L., Boser, B., Denker, J., Graf, H., Guyon, I., Henderson, D., Howard, R., & Hubbard, W. (1989). Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Communications Magazine, 27(11), 41–46. https://doi.org/10.1109/35.41400
  • ILSVRC2014 Results. (n.d.). ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014). Retrieved February 2022, from https://image-net.org/challenges/LSVRC/2014/results
  • Farfade, S. S., Saberian, M. J., & Li, L. J. (2015). Multi-view Face Detection Using Deep Convolutional Neural Networks. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. https://doi.org/10.1145/2671188.2749408
  • Burkov, A. (2019). The Hundred-Page Machine Learning Book. Andriy Burkov.
  • Gao, S., & Lima, D. (2022). A review of the application of deep learning in the detection of Alzheimer’s disease. International Journal of Cognitive Computing in Engineering, 3, 1–8. https://doi.org/10.1016/j.ijcce.2021.12.002
  • Zhang, Y. D., Govindaraj, V. V., Tang, C., Zhu, W., & Sun, J. (2019). High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model. Journal of Medical Imaging and Health Informatics, 9(9), 2012–2021. https://doi.org/10.1166/jmihi.2019.2692
  • Zhang, Y., Guttery, D., & Wang, S. H. (2020). 90P Abnormal breast detection by an improved AlexNet model. Annals of Oncology, 31, S277. https://doi.org/10.1016/j.annonc.2020.08.211
  • Lu, S., Lu, Z., & Zhang, Y. D. (2019). Pathological brain detection based on AlexNet and transfer learning. Journal of Computational Science, 30, 41–47. https://doi.org/10.1016/j.jocs.2018.11.008
  • Wang, S. H., Xie, S., Chen, X., Guttery, D. S., Tang, C., Sun, J., & Zhang, Y. D. (2019). Alcoholism Identification Based on an AlexNet Transfer Learning Model. Frontiers in Psychiatry, 10. https://doi.org/10.3389/fpsyt.2019.00205
  • Alotaibi, B., & Alotaibi, M. (2020). A Hybrid Deep ResNet and Inception Model for Hyperspectral Image Classification. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(6), 463–476. https://doi.org/10.1007/s41064-020-00124-x
  • Firdaus, N. M. , Chahyati, D. , & Fanany, M. I. (2018). Ieee, "Tourist Attractions Classification using ResNet. In Proceedings of the 10th international conference on advanced computer science and information systems (ICACSIS) .
  • Zhang, Y. D., Satapathy, S. C., Zhang, X., & Wang, S. H. (2021). COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Cognitive Computation. https://doi.org/10.1007/s12559-020-09776-8
  • Wang, S. H., & Zhang, Y. D. (2020). DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2s), 1–19. https://doi.org/10.1145/3341095
  • Puttagunta, M., & Ravi, S. (2021). Medical image analysis based on deep learning approach. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-10707-4
  • Yang, K. & Mohammed, E. (2020). A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer’s Disease. arXiv.Org. https://arxiv.org/abs/2101.01781
  • Huang, Z., Zhu, X., Ding, M., & Zhang, X. (2020). Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet. IEEE Access, 8, 24697–24712. https://doi.org/10.1109/access.2020.2971225
  • D. (n.d.). DLTK Input normalisation and augmentation. GitHub. Retrieved February 21, 2022, from https://github.com/DLTK/DLTK/blob/master/examples/tutorials/04_input_normalisation_and_augmentation.ipynb
  • Zhang, F., Tian, S., Chen, S., Ma, Y., Li, X., & Guo, X. (2019). Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer’s Disease Based on an Extreme Learning Machine Method from the ADNI cohort. Neuroscience, 414, 273–279. https://doi.org/10.1016/j.neuroscience.2019.05.014
  • Ortiz, A., Munilla, J., Górriz, J. M., & Ramírez, J. (2016). Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease. International Journal of Neural Systems, 26(07), 1650025. https://doi.org/10.1142/s0129065716500258
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A Model Suggestion For Alzheimer’s Disease Diagnosis By Using Deep Learning

Yıl 2022, Sayı: 37, 123 - 130, 15.07.2022
https://doi.org/10.31590/ejosat.1136855

Öz

Alzheimer's disease is one of the greatest health problems of our time. Since there is no cure, it is necessary to diagnose the disease in the early stages and to apply preventive treatments. However, early diagnosis of the disease is very difficult, so most people can be diagnosed after significant and irreversible effects occur. Various studies are carried out by researchers around the world for the early diagnosis of the disease. Deep learning has recently gained importance in the early diagnosis of Alzheimer's disease. With the use of models created using deep learning, the success of early diagnosis has reached high levels. In this study, the stages of Alzheimer's disease and the changes that occur were examined. A literature review was conducted for various techniques used in the diagnosis of Alzheimer's and the use of imaging techniques in the early diagnosis of Alzheimer's was investigated. Due to its widespread use, the MRI technique has been emphasized, and mostly studies using MRI have been examined. Concepts used in deep learning are explained, innovations and results are presented. The architectures used in deep learning and the innovations they bring to this field are revealed, and deep learning models that have been created and tested in current studies are examined. The innovations and success rates brought by various studies have been revealed. Efforts have been made to develop a fast, efficient and successful model that provides ease of use. For this, the scheduler structure, MONAI framework, Data loader structure and various techniques are presented with simple use. Also, the model is optimized to run smoothly on Google Colab. In addition, the tools in the FSL library, which are very important in preprocessing, were studied and optimal parameters were found for the "Bias field and Neck Clean Up", "Standard Brain Extraction Using BET2" and "Robust Brain Center Estimation" tools. By using this library, optimal brain images can be obtained for any model. The DenseNet121 model was used as a basis in the model and it was presented in a structure that can be easily changed. The model can directly use 3D MR images, thus preventing the loss of various spatial information.

Kaynakça

  • Wee, C. Y., Yap, P. T., & Shen, D. (2012). Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping, 34(12), 3411–3425. https://doi.org/10.1002/hbm.22156
  • Bain LJ, Jedrziewski K, Morrison-Bogorad M, Albert M, Cotman C, Hendrie H, Trojanowski JQ (2008): Healthy brain aging: A meeting report from the Sylvan M. Cohen Annual Retreat of The University of Pennsylvania Institute On Aging. Alzheimers Dement 4:443–446.
  • Grundman M, Petersen RC, Ferris SH, Thomas RG, Aisen PS, Bennett DA, et al. (2004): Mild cognitive impairment can be distinguished from Alzheimer’s disease and normal aging for clinical trials. Arch Neurol 61:59–66.
  • Misra C, Fan Y, Davatzikos C (2009): Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI. Neuroimage 44:1414–1422.
  • Sarraf, S., & Tofighi, G. (2016). Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. 2016 Future Technologies Conference (FTC). https://doi.org/10.1109/ftc.2016.7821697
  • Kam, T. E., Zhang, H., & Shen, D. (2018). A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 293–301. https://doi.org/10.1007/978-3-030-00931-1_34
  • Yan, W., Zhang, H., Sui, J., & Shen, D. (2018). Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 249–257. https://doi.org/10.1007/978-3-030-00931-1_29
  • Dyrba, M., Barkhof, F., Fellgiebel, A., Filippi, M., Hausner, L., Hauenstein, K., Kirste, T., & Teipel, S. J. (2015). Predicting Prodromal Alzheimer’s Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of Neuroimaging, 25(5), 738–747. https://doi.org/10.1111/jon.12214
  • Zhang, Y., Teng, Q., Liu, Y., Liu, Y., & He, X. (2022). Diagnosis of Alzheimer’s disease based on regional attention with sMRI gray matter slices. Journal of Neuroscience Methods, 365, 109376. https://doi.org/10.1016/j.jneumeth.2021.109376
  • Li, W., Lin, X., & Chen, X. (2020). Detecting Alzheimer’s disease Based on 4D fMRI: An exploration under deep learning framework. Neurocomputing, 388, 280–287. https://doi.org/10.1016/j.neucom.2020.01.053
  • Ding, Y., Sohn, J. H., Kawczynski, M. G., Trivedi, H., Harnish, R., Jenkins, N. W., Lituiev, D., Copeland, T. P., Aboian, M. S., Mari Aparici, C., Behr, S. C., Flavell, R. R., Huang, S. Y., Zalocusky, K. A., Nardo, L., Seo, Y., Hawkins, R. A., Hernandez Pampaloni, M., Hadley, D., & Franc, B. L. (2019). A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology, 290(2), 456–464. https://doi.org/10.1148/radiol.2018180958
  • Dyrba, M., Ewers, M., Wegrzyn, M., Kilimann, I., Plant, C., Oswald, A., Meindl, T., Pievani, M., Bokde, A. L. W., Fellgiebel, A., Filippi, M., Hampel, H., Klöppel, S., Hauenstein, K., Kirste, T., & Teipel, S. J. (2013). Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer’s Disease Using Machine Learning Classification of Multicenter DTI Data. PLoS ONE, 8(5), e64925. https://doi.org/10.1371/journal.pone.0064925
  • Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X., & Zhao, X. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing, 333, 145–156. https://doi.org/10.1016/j.neucom.2018.12.018
  • Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., Maqsood, M., & Yaqub, M. (2021). A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images. Neuroscience, 460, 43–52. https://doi.org/10.1016/j.neuroscience.2021.01.002
  • ADNI | Alzheimer’s Disease Neuroimaging Initiative. (n.d.). Alzheimer’s Disease Neuroimaging Initiative. Retrieved January 18, 2022, from https://adni.loni.usc.edu/
  • ADNI | Alzheimer’s Disease Neuroimaging Initiative. (n.d.). Alzheimer’s Disease Neuroimaging Initiative. Retrieved January 18, 2022, from https://ida.loni.usc.edu/home/projectPage.jsp?project=ADNI&page=HOME&subPage=OVERVIEW_PR
  • FSL - FslWiki. (n.d.). FMRIB Software Library. Retrieved March 23, 2022, from https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL
  • M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86, 2009
  • S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004
  • S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002.
  • M. Jenkinson, M. Pechaud, and S. Smith. BET2: MR-based estimation of brain, skull and scalp surfaces. In Eleventh Annual Meeting of the Organization for Human Brain Mapping, 2005.
  • Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An Introductory Review of Deep Learning for Prediction Models With Big Data. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00004
  • Scherer, D., Müller, A., & Behnke, S. (2010). Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. Artificial Neural Networks – ICANN 2010, 92–101. https://doi.org/10.1007/978-3-642-15825-4_10
  • Le Cun, Y., Jackel, L., Boser, B., Denker, J., Graf, H., Guyon, I., Henderson, D., Howard, R., & Hubbard, W. (1989). Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Communications Magazine, 27(11), 41–46. https://doi.org/10.1109/35.41400
  • ILSVRC2014 Results. (n.d.). ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014). Retrieved February 2022, from https://image-net.org/challenges/LSVRC/2014/results
  • Farfade, S. S., Saberian, M. J., & Li, L. J. (2015). Multi-view Face Detection Using Deep Convolutional Neural Networks. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. https://doi.org/10.1145/2671188.2749408
  • Burkov, A. (2019). The Hundred-Page Machine Learning Book. Andriy Burkov.
  • Gao, S., & Lima, D. (2022). A review of the application of deep learning in the detection of Alzheimer’s disease. International Journal of Cognitive Computing in Engineering, 3, 1–8. https://doi.org/10.1016/j.ijcce.2021.12.002
  • Zhang, Y. D., Govindaraj, V. V., Tang, C., Zhu, W., & Sun, J. (2019). High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model. Journal of Medical Imaging and Health Informatics, 9(9), 2012–2021. https://doi.org/10.1166/jmihi.2019.2692
  • Zhang, Y., Guttery, D., & Wang, S. H. (2020). 90P Abnormal breast detection by an improved AlexNet model. Annals of Oncology, 31, S277. https://doi.org/10.1016/j.annonc.2020.08.211
  • Lu, S., Lu, Z., & Zhang, Y. D. (2019). Pathological brain detection based on AlexNet and transfer learning. Journal of Computational Science, 30, 41–47. https://doi.org/10.1016/j.jocs.2018.11.008
  • Wang, S. H., Xie, S., Chen, X., Guttery, D. S., Tang, C., Sun, J., & Zhang, Y. D. (2019). Alcoholism Identification Based on an AlexNet Transfer Learning Model. Frontiers in Psychiatry, 10. https://doi.org/10.3389/fpsyt.2019.00205
  • Alotaibi, B., & Alotaibi, M. (2020). A Hybrid Deep ResNet and Inception Model for Hyperspectral Image Classification. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(6), 463–476. https://doi.org/10.1007/s41064-020-00124-x
  • Firdaus, N. M. , Chahyati, D. , & Fanany, M. I. (2018). Ieee, "Tourist Attractions Classification using ResNet. In Proceedings of the 10th international conference on advanced computer science and information systems (ICACSIS) .
  • Zhang, Y. D., Satapathy, S. C., Zhang, X., & Wang, S. H. (2021). COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Cognitive Computation. https://doi.org/10.1007/s12559-020-09776-8
  • Wang, S. H., & Zhang, Y. D. (2020). DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2s), 1–19. https://doi.org/10.1145/3341095
  • Puttagunta, M., & Ravi, S. (2021). Medical image analysis based on deep learning approach. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-10707-4
  • Yang, K. & Mohammed, E. (2020). A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer’s Disease. arXiv.Org. https://arxiv.org/abs/2101.01781
  • Huang, Z., Zhu, X., Ding, M., & Zhang, X. (2020). Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet. IEEE Access, 8, 24697–24712. https://doi.org/10.1109/access.2020.2971225
  • D. (n.d.). DLTK Input normalisation and augmentation. GitHub. Retrieved February 21, 2022, from https://github.com/DLTK/DLTK/blob/master/examples/tutorials/04_input_normalisation_and_augmentation.ipynb
  • Zhang, F., Tian, S., Chen, S., Ma, Y., Li, X., & Guo, X. (2019). Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer’s Disease Based on an Extreme Learning Machine Method from the ADNI cohort. Neuroscience, 414, 273–279. https://doi.org/10.1016/j.neuroscience.2019.05.014
  • Ortiz, A., Munilla, J., Górriz, J. M., & Ramírez, J. (2016). Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease. International Journal of Neural Systems, 26(07), 1650025. https://doi.org/10.1142/s0129065716500258
  • Google Colaboratory. (n.d.). Google Colaboratory. Retrieved January 13, 2022, from https://colab.research.google.com/
  • Project MONAI. (n.d.). GitHub. Retrieved January 21, 2022, from https://github.com/Project-MONAI
  • MONAI. (n.d.). Medical Open Network for Artificial Intelligence. Retrieved January 21, 2022, from https://monai.io/index.html
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Anıl Özkaya 0000-0002-5226-9955

Ufuk Cebeci 0000-0003-4367-6206

Erken Görünüm Tarihi 30 Haziran 2022
Yayımlanma Tarihi 15 Temmuz 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 37

Kaynak Göster

APA Özkaya, A., & Cebeci, U. (2022). A Model Suggestion For Alzheimer’s Disease Diagnosis By Using Deep Learning. Avrupa Bilim Ve Teknoloji Dergisi(37), 123-130. https://doi.org/10.31590/ejosat.1136855