Araştırma Makalesi
BibTex RIS Kaynak Göster

Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz

Yıl 2023, Cilt: 13 Sayı: 3, 1454 - 1467, 01.09.2023

Selahattin Barış Çelebi Bülent Gürsel Emiroğlu

https://doi.org/10.21597/jist.1275669
Cited By: 1

Öz

Alzheimer, dünyadaki en yaygın bunama türüdür ve şu an için kullanılan tedavi yöntemleri sadece hastalığın ilerleyişini önleme amacına yöneliktir. Beyin dokusu hacmi Alzheimer hastalığı (AD) nedeniyle değişir. Tensör tabanlı morfometri (TBM) yardımıyla, hastalığın beyin dokularında neden olduğu değişiklikler izlenebilir. Bu çalışmada AD hastaları ve Bilişsel Normal(ler) (CN'ler) grubu denekleri arasında ayrım yapmak için etkili bir yöntem geliştirmek amaçlanmıştır. TBM veya küçük yerel hacim farklılıkları, sınıflandırma özelliği olarak benimsenmiştir. AD/CN sınıfına ait 3D TBM morfometrik görüntülerinden hipokampus ve temporal lobu kapsayan 5 piksel aralıklı eksenel beyin görüntü dilimleri 2D olarak kaydedildi. Daha sonra her bir klinik gruptan (AD; CN) elde edilen veri setinin %60'ı eğitim, %20’si validasyon ve %20’si test veri setleri olarak ayrıldı (Eğitim: 480; doğrulama: 120; test: 120). Model validasyon (%92.5) ve test (%89) doğruluk değerleri ile AD/CN tahmini gerçekleştirdi. Sonuçlar, Derin öğrenme ile hipokampus ve temporal lobu kapsayan dilimlerden elde edilen TBM'nin AD'nin tanısında yüksek doğrulukla uygulanabileceğini göstermektedir.

Anahtar Kelimeler

Tensor tabanlı morfometri Alzheimer Derin Öğrenme Veri artırımı

Kaynakça

  • Aljović, A., Badnjević, A., & Gurbeta, L. (2016). Artificial neural networks in the discrimination of Alzheimer’s disease using biomarkers data. 2016 5th Mediterranean Conference on Embedded Computing, MECO 2016 - Including ECyPS 2016, BIOENG.MED 2016, MECO: Student Challenge 2016, 286–289. Retrieved from https://doi.org/10.1109/MECO.2016.7525762
  • Alsop, D. C., Casement, M., De Bazelaire, C., Fong, T., & Press, D. Z. (2008). Hippocampal hyperperfusion in Alzheimer’s disease. NeuroImage, 42(4), 1267–1274. Retrieved from https://doi.org/10.1016/J.NEUROIMAGE.2008.06.006
  • Altinkaya, E., Polat, K., Barakli, B., & Author, C. (2020). Detection of Alzheimer’s Disease and Dementia States Based on Deep Learning from MRI Images: A Comprehensive Review. Journal of the Institute of Electronics and Computer, 1(1), 39–53. Retrieved 31 March 2023 from https://doi.org/10.33969/JIEC.2019.11005
  • Arnsten, A. F. T., Datta, D., Del Tredici, K., & Braak, H. (2021). Hypothesis: Tau pathology is an initiating factor in sporadic Alzheimer’s disease. Alzheimer’s and Dementia, 17(1). Retrieved from https://doi.org/10.1002/alz.12192
  • Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry - The methods. NeuroImage, 11(6 I). Retrieved from https://doi.org/10.1006/nimg.2000.0582
  • Aslan, A., & Çelebi, S. B. (2022). Real Time Deep Learning Based Age and Gender Detection For Advertising and Marketing. In H. İş & İ. Demir (Eds.), Uluslararası Bilişim Kongresi (IIC 2022): bildiriler kitabı (pp. 10–16). Batman: https://hdl.handle.net/20.500.12402/4205.
  • Birecikli, B., Karaman, Ö. A., Çelebi, S. B., & Turgut, A. (2020). Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks. Journal of Mechanical Science and Technology, 34(11), 4631–4640. Retrieved 31 March 2023 from https://doi.org/10.1007/s12206-020-1021-7
  • Brambati, S. M., Renda, N. C., Rankin, K. P., Rosen, H. J., Seeley, W. W., Ashburner, J., … Gorno-Tempini, M. L. (2007). A tensor based morphometry study of longitudinal gray matter contraction in FTD. NeuroImage, 35(3). Retrieved from https://doi.org/10.1016/j.neuroimage.2007.01.028
  • Buvaneswari, P. R., & Gayathri, R. (2021). Deep Learning-Based Segmentation in Classification of Alzheimer’s Disease. Arabian Journal for Science and Engineering, 46(6), 5373–5383. Retrieved 31 March 2023 from https://doi.org/10.1007/S13369-020-05193-Z/TABLES/5
  • Jack Jr, C. R., Barnes, J., Bernstein, M. A., Borowski, B. J., Brewer, J., Clegg, S., ... & Weiner, M. (2015). Magnetic resonance imaging in Alzheimer's disease neuroimaging initiative 2. Alzheimer's & Dementia, 11(7), 740-756.
  • Çalişkan, A., & Çevik, U. (2018). An efficient noisy pixels detection model for CT images using extreme learning machines. Tehnicki Vjesnik, 25(3). Retrieved from https://doi.org/10.17559/TV-20171220221947
  • Çalışkan, A., Demirhan, S., & Tekin, R. (2022). Comparison of different machine learning methods for estimating compressive strength of mortars. Construction and Building Materials, 335, 127490. Retrieved from https://doi.org/10.1016/J.CONBUILDMAT.2022.127490
  • Aslan, A., & Çelebi, S. B. (2022). Machine Learning Based Audio Classifıcation. In H. İş & İ. Demir (Eds.), Uluslararası Bilişim Kongresi (IIC 2022): bildiriler kitabı (pp. 119–122). Batman: https://hdl.handle.net/20.500.12402/4205.
  • Çetiner, H., & Çetiner, İ. (2022). Classification of Cataract Disease with a DenseNet201 Based Deep Learning Model. Journal of the Institute of Science and Technology, 12(3), 1264–1276. Retrieved 1 April 2023 from https://doi.org/10.21597/JIST.1098718
  • Chen, S., Zhang, J., Wei, X., & Zhang, Q. (2020). Alzheimer’s Disease Classification Using Structural MRI Based on Convolutional Neural Networks. ACM International Conference Proceeding Series, 7–13. Retrieved 31 March 2023 from https://doi.org/10.1145/3440054.3440056
  • Cinarer, G., & Emiroglu, B. G. (2019). Classificatin of Brain Tumors by Machine Learning Algorithms. In 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedings. Retrieved from https://doi.org/10.1109/ISMSIT.2019.8932878
  • Çinarer, G., Gürsel, B., & Haşim, A. (2020). Prediction of glioma grades using deep learning with wavelet radiomic features. Applied Sciences (Switzerland), 10(18). Retrieved from https://doi.org/10.3390/APP10186296
  • Cruz, R. S., Lebrat, L., Bourgeat, P., Dore, V., Dowling, J., Fripp, J., … Salvado, O. (2021). Going deeper with brain morphometry using neural networks. Proceedings - International Symposium on Biomedical Imaging, 2021-April, 711–715. Retrieved from https://doi.org/10.1109/ISBI48211.2021.9434039
  • Erkan, E., & Arserim, M. A. (2022). Mobile Robot Application with Hierarchical Start Position DQN. Computational Intelligence and Neuroscience, 2022. Retrieved from https://doi.org/10.1155/2022/4115767
  • Farina, Nicolas, David Llewellyn, Mokhtar Gad El Kareem Nasr Isaac, and Naji Tabet. (2017) “Vitamin E for Alzheimer’s Dementia and Mild Cognitive Impairment.” The Cochrane Database of Systematic Reviews
  • Farouk, Y., Rady, S., & Faheem, H. (2018). Statistical features and voxel-based morphometry for Alzheimer’s disease classification. 2018 9th International Conference on Information and Communication Systems, ICICS 2018, 2018-January, 133–138. Retrieved from https://doi.org/10.1109/IACS.2018.8355455
  • Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. Retrieved from https://doi.org/10.1016/J.ENGAPPAI.2022.105151
  • 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. Retrieved from https://doi.org/10.1016/j.ijcce.2021.12.002
  • Gupta, Y., Lee, K. H., Choi, K. Y., Lee, J. J., Kim, B. C., & Kwon, G. R. (2019). National Research Center for Dementia; Alzheimer’s Disease Neuroimaging Initiative. Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images. PloS One, 14, e0222446.
  • Hedayati, R., Khedmati, M., & Taghipour-Gorjikolaie, M. (2021). Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis. Biomedical Signal Processing and Control, 66, 102397. Retrieved from https://doi.org/10.1016/J.BSPC.2020.102397
  • Hua, X., Hibar, D. P., Ching, C. R. K., Boyle, C. P., Rajagopalan, P., Gutman, B. A., … Thompson, P. M. (2013). Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer’s disease clinical trials. NeuroImage, 66, 648–661. Retrieved from https://doi.org/10.1016/J.NEUROIMAGE.2012.10.086
  • Hua, X., Leow, A. D., Parikshak, N., Lee, S., Chiang, M. C., Toga, A. W., … Thompson, P. M. (2008). Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: An MRI study of 676 AD, MCI, and normal subjects. NeuroImage, 43(3), 458–469. Retrieved from https://doi.org/10.1016/J.NEUROIMAGE.2008.07.013
  • Karaman, Ö. A., Tanyıldızı Ağır, T., & Arsel, İ. (2021). Estimation of solar radiation using modern methods. Alexandria Engineering Journal, 60(2). Retrieved from https://doi.org/10.1016/j.aej.2020.12.048
  • Karaman, A., Pacal, I., Basturk, A., Akay, B., Nalbantoglu, U., Coskun, S., Sahin, O., Karaboga, D. (2023). Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC). Expert Systems with Applications, 221, 119741. Retrieved from https://doi.org/10.1016/j.eswa.2023.119741
  • Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., … Frackowiak, R. S. J. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain, 131(3). Retrieved from https://doi.org/10.1093/brain/awm319
  • Koikkalainen, J., Lötjönen, J., Thurfjell, L., NeuroImage, D. R.-, & 2011, undefined. (n.d.). Multi-template tensor-based morphometry: application to analysis of Alzheimer’s disease. Elsevier. Retrieved 31 March 2023 from https://www.sciencedirect.com/science/article/pii/S1053811911003053
  • Krstinić, D., Braović, M., Šerić, L., & Božić-Štulić, D. (2020). Multi-label Classifier Performance Evaluation with Confusion Matrix. Retrieved from https://doi.org/10.5121/csit.2020.100801
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
  • Kumari, R., Nigam, A., & Pushkar, S. (2022). An efficient combination of quadruple biomarkers in binary classification using ensemble machine learning technique for early onset of Alzheimer disease. Neural Computing and Applications, 34(14). Retrieved from https://doi.org/10.1007/s00521-022-07076-w
  • Lai, K. L., Niddam, D. M., Fuh, J. L., Chen, W. T., Wu, J. C., & Wang, S. J. (2020). Cortical morphological changes in chronic migraine in a Taiwanese cohort: Surface- and voxel-based analyses. Cephalalgia, 40(6). Retrieved from https://doi.org/10.1177/0333102420920005
  • Ledig, C., Schuh, A., Guerrero, R., Heckemann, R. A., & Rueckert, D. (2018). Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Reports 2018 8:1, 8(1), 1–16. Retrieved 31 March 2023 from https://doi.org/10.1038/s41598-018-29295-9
  • Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis. Retrieved from https://doi.org/10.1016/j.media.2017.07.005
  • Manera, A. L., Dadar, M., Collins, D. L., & Ducharme, S. (2019). Deformation based morphometry study of longitudinal MRI changes in behavioral variant frontotemporal dementia. NeuroImage: Clinical, 24. Retrieved from https://doi.org/10.1016/j.nicl.2019.102079
  • Mustafa Abdullah, D., & Mohsin Abdulazeez, A. (2021). Machine Learning Applications based on SVM Classification A Review. Qubahan Academic Journal, 1(2). Retrieved from https://doi.org/10.48161/qaj.v1n2a50
  • Nir, T. M., Villalon-Reina, J. E., Prasad, G., Jahanshad, N., Joshi, S. H., Toga, A. W., … Thompson, P. M. (2015). Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer’s disease. Neurobiology of Aging, 36(S1), S132–S140. Retrieved from https://doi.org/10.1016/J.NEUROBIOLAGING.2014.05.037
  • Othman, N. A., & Aydin, I. (2022). A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies. Traitement Du Signal, 39(6), 1951–1959. Retrieved from https://doi.org/10.18280/TS.390607
  • Pienaar, R. (n.d.). med2image. Retrieved 31 March 2023 from https://github.com/FNNDSC/med2image
  • Plasensia, O. E. . (2019). Personalized Medicine: Comparison of Techniques for the Automatic Diagnosis of Alzheimer’s Disease. Unir la Universidad En Internet.
  • Pacal, I., & Karaboga, D. (2021). A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134, 104519.Retrieved from https://doi.org/10.1016/j.compbiomed.2021.104519
  • Pacal, İ. Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images. Journal of the Institute of Science and Technology, 12(4), 1917-1927. Retrieved from https://doi.org/10.21597/jist.1183679
  • Savaş, S. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218. Retrieved 31 March 2023 from https://doi.org/10.1007/S13369-021-06131-3/TABLES/5
  • Wu, L., Rosa-Neto, P., & Gauthier, S. (2011). Use of biomarkers in clinical trials of alzheimer disease: From concept to application. Molecular Diagnosis and Therapy. Retrieved from https://doi.org/10.2165/11595090-000000000-00000
  • Yaman, O., & Tuncer, T. (2022). Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images. Biomedical Signal Processing and Control, 73. Retrieved from https://doi.org/10.1016/j.bspc.2021.103428
  • Yang, S. T., Lee, J. Der, Huang, C. H., Wang, J. J., Hsu, W. C., & Wai, Y. Y. (2010). Computer-aided diagnosis of Alzheimer’s disease using multiple features with artificial neural network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6230 LNAI, 699–705. Retrieved 31 March 2023 from https://doi.org/10.1007/978-3-642-15246-7_72/COVER
  • 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. Retrieved from https://doi.org/10.1016/J.NEUROSCIENCE.2019.05.014
  • Zhang, J., & Shi, S. (2013). A literature review of AD7c-ntp as a biomarker for Alzheimer’s disease. Annals of Indian Academy of Neurology. Retrieved from https://doi.org/10.4103/0972-2327.116902

Deep Learning Based Morphometric Analysis for Alzheimer's Diagnosis

Yıl 2023, Cilt: 13 Sayı: 3, 1454 - 1467, 01.09.2023

Selahattin Barış Çelebi Bülent Gürsel Emiroğlu

https://doi.org/10.21597/jist.1275669
Cited By: 1

Öz

Alzheimer's disease is the most common type of dementia in the world, and the treatment methods currently used are aimed only at preventing the progression of the disease. Brain tissue volume changes due to Alzheimer's disease (AD). With the help of tensor-based morphometry (TBM), changes in brain tissues caused by the disease can be monitored. This study aimed to develop an effective method to differentiate between AD patients and the Cognitive Normal (CN) group subjects. TBM, or small local volume differences, are adopted as classification features. Axial brain image slices with 5-pixel intervals covering the hippocampus and temporal lobe from 3D TBM morphometric images belonging to the AD/CN class were recorded in 2D. Then, 60% of the dataset obtained from each clinical group (AD; CN) was allocated as training, 20% as validation, and 20% as test datasets (training: 480; validation: 120; testing: 120). The model performed AD/CN estimation with validation (92.5%) and testing (89%) accuracy values. The results show that TBM obtained from slices covering the hippocampus and temporal lobe with deep learning can be applied with high accuracy in the diagnosis of AD.

Anahtar Kelimeler

Alzheimer Data augmentation Deep Learning Tensor-based morphometry

Kaynakça

  • Aljović, A., Badnjević, A., & Gurbeta, L. (2016). Artificial neural networks in the discrimination of Alzheimer’s disease using biomarkers data. 2016 5th Mediterranean Conference on Embedded Computing, MECO 2016 - Including ECyPS 2016, BIOENG.MED 2016, MECO: Student Challenge 2016, 286–289. Retrieved from https://doi.org/10.1109/MECO.2016.7525762
  • Alsop, D. C., Casement, M., De Bazelaire, C., Fong, T., & Press, D. Z. (2008). Hippocampal hyperperfusion in Alzheimer’s disease. NeuroImage, 42(4), 1267–1274. Retrieved from https://doi.org/10.1016/J.NEUROIMAGE.2008.06.006
  • Altinkaya, E., Polat, K., Barakli, B., & Author, C. (2020). Detection of Alzheimer’s Disease and Dementia States Based on Deep Learning from MRI Images: A Comprehensive Review. Journal of the Institute of Electronics and Computer, 1(1), 39–53. Retrieved 31 March 2023 from https://doi.org/10.33969/JIEC.2019.11005
  • Arnsten, A. F. T., Datta, D., Del Tredici, K., & Braak, H. (2021). Hypothesis: Tau pathology is an initiating factor in sporadic Alzheimer’s disease. Alzheimer’s and Dementia, 17(1). Retrieved from https://doi.org/10.1002/alz.12192
  • Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry - The methods. NeuroImage, 11(6 I). Retrieved from https://doi.org/10.1006/nimg.2000.0582
  • Aslan, A., & Çelebi, S. B. (2022). Real Time Deep Learning Based Age and Gender Detection For Advertising and Marketing. In H. İş & İ. Demir (Eds.), Uluslararası Bilişim Kongresi (IIC 2022): bildiriler kitabı (pp. 10–16). Batman: https://hdl.handle.net/20.500.12402/4205.
  • Birecikli, B., Karaman, Ö. A., Çelebi, S. B., & Turgut, A. (2020). Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks. Journal of Mechanical Science and Technology, 34(11), 4631–4640. Retrieved 31 March 2023 from https://doi.org/10.1007/s12206-020-1021-7
  • Brambati, S. M., Renda, N. C., Rankin, K. P., Rosen, H. J., Seeley, W. W., Ashburner, J., … Gorno-Tempini, M. L. (2007). A tensor based morphometry study of longitudinal gray matter contraction in FTD. NeuroImage, 35(3). Retrieved from https://doi.org/10.1016/j.neuroimage.2007.01.028
  • Buvaneswari, P. R., & Gayathri, R. (2021). Deep Learning-Based Segmentation in Classification of Alzheimer’s Disease. Arabian Journal for Science and Engineering, 46(6), 5373–5383. Retrieved 31 March 2023 from https://doi.org/10.1007/S13369-020-05193-Z/TABLES/5
  • Jack Jr, C. R., Barnes, J., Bernstein, M. A., Borowski, B. J., Brewer, J., Clegg, S., ... & Weiner, M. (2015). Magnetic resonance imaging in Alzheimer's disease neuroimaging initiative 2. Alzheimer's & Dementia, 11(7), 740-756.
  • Çalişkan, A., & Çevik, U. (2018). An efficient noisy pixels detection model for CT images using extreme learning machines. Tehnicki Vjesnik, 25(3). Retrieved from https://doi.org/10.17559/TV-20171220221947
  • Çalışkan, A., Demirhan, S., & Tekin, R. (2022). Comparison of different machine learning methods for estimating compressive strength of mortars. Construction and Building Materials, 335, 127490. Retrieved from https://doi.org/10.1016/J.CONBUILDMAT.2022.127490
  • Aslan, A., & Çelebi, S. B. (2022). Machine Learning Based Audio Classifıcation. In H. İş & İ. Demir (Eds.), Uluslararası Bilişim Kongresi (IIC 2022): bildiriler kitabı (pp. 119–122). Batman: https://hdl.handle.net/20.500.12402/4205.
  • Çetiner, H., & Çetiner, İ. (2022). Classification of Cataract Disease with a DenseNet201 Based Deep Learning Model. Journal of the Institute of Science and Technology, 12(3), 1264–1276. Retrieved 1 April 2023 from https://doi.org/10.21597/JIST.1098718
  • Chen, S., Zhang, J., Wei, X., & Zhang, Q. (2020). Alzheimer’s Disease Classification Using Structural MRI Based on Convolutional Neural Networks. ACM International Conference Proceeding Series, 7–13. Retrieved 31 March 2023 from https://doi.org/10.1145/3440054.3440056
  • Cinarer, G., & Emiroglu, B. G. (2019). Classificatin of Brain Tumors by Machine Learning Algorithms. In 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedings. Retrieved from https://doi.org/10.1109/ISMSIT.2019.8932878
  • Çinarer, G., Gürsel, B., & Haşim, A. (2020). Prediction of glioma grades using deep learning with wavelet radiomic features. Applied Sciences (Switzerland), 10(18). Retrieved from https://doi.org/10.3390/APP10186296
  • Cruz, R. S., Lebrat, L., Bourgeat, P., Dore, V., Dowling, J., Fripp, J., … Salvado, O. (2021). Going deeper with brain morphometry using neural networks. Proceedings - International Symposium on Biomedical Imaging, 2021-April, 711–715. Retrieved from https://doi.org/10.1109/ISBI48211.2021.9434039
  • Erkan, E., & Arserim, M. A. (2022). Mobile Robot Application with Hierarchical Start Position DQN. Computational Intelligence and Neuroscience, 2022. Retrieved from https://doi.org/10.1155/2022/4115767
  • Farina, Nicolas, David Llewellyn, Mokhtar Gad El Kareem Nasr Isaac, and Naji Tabet. (2017) “Vitamin E for Alzheimer’s Dementia and Mild Cognitive Impairment.” The Cochrane Database of Systematic Reviews
  • Farouk, Y., Rady, S., & Faheem, H. (2018). Statistical features and voxel-based morphometry for Alzheimer’s disease classification. 2018 9th International Conference on Information and Communication Systems, ICICS 2018, 2018-January, 133–138. Retrieved from https://doi.org/10.1109/IACS.2018.8355455
  • Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. Retrieved from https://doi.org/10.1016/J.ENGAPPAI.2022.105151
  • 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. Retrieved from https://doi.org/10.1016/j.ijcce.2021.12.002
  • Gupta, Y., Lee, K. H., Choi, K. Y., Lee, J. J., Kim, B. C., & Kwon, G. R. (2019). National Research Center for Dementia; Alzheimer’s Disease Neuroimaging Initiative. Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images. PloS One, 14, e0222446.
  • Hedayati, R., Khedmati, M., & Taghipour-Gorjikolaie, M. (2021). Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis. Biomedical Signal Processing and Control, 66, 102397. Retrieved from https://doi.org/10.1016/J.BSPC.2020.102397
  • Hua, X., Hibar, D. P., Ching, C. R. K., Boyle, C. P., Rajagopalan, P., Gutman, B. A., … Thompson, P. M. (2013). Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer’s disease clinical trials. NeuroImage, 66, 648–661. Retrieved from https://doi.org/10.1016/J.NEUROIMAGE.2012.10.086
  • Hua, X., Leow, A. D., Parikshak, N., Lee, S., Chiang, M. C., Toga, A. W., … Thompson, P. M. (2008). Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: An MRI study of 676 AD, MCI, and normal subjects. NeuroImage, 43(3), 458–469. Retrieved from https://doi.org/10.1016/J.NEUROIMAGE.2008.07.013
  • Karaman, Ö. A., Tanyıldızı Ağır, T., & Arsel, İ. (2021). Estimation of solar radiation using modern methods. Alexandria Engineering Journal, 60(2). Retrieved from https://doi.org/10.1016/j.aej.2020.12.048
  • Karaman, A., Pacal, I., Basturk, A., Akay, B., Nalbantoglu, U., Coskun, S., Sahin, O., Karaboga, D. (2023). Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC). Expert Systems with Applications, 221, 119741. Retrieved from https://doi.org/10.1016/j.eswa.2023.119741
  • Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., … Frackowiak, R. S. J. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain, 131(3). Retrieved from https://doi.org/10.1093/brain/awm319
  • Koikkalainen, J., Lötjönen, J., Thurfjell, L., NeuroImage, D. R.-, & 2011, undefined. (n.d.). Multi-template tensor-based morphometry: application to analysis of Alzheimer’s disease. Elsevier. Retrieved 31 March 2023 from https://www.sciencedirect.com/science/article/pii/S1053811911003053
  • Krstinić, D., Braović, M., Šerić, L., & Božić-Štulić, D. (2020). Multi-label Classifier Performance Evaluation with Confusion Matrix. Retrieved from https://doi.org/10.5121/csit.2020.100801
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
  • Kumari, R., Nigam, A., & Pushkar, S. (2022). An efficient combination of quadruple biomarkers in binary classification using ensemble machine learning technique for early onset of Alzheimer disease. Neural Computing and Applications, 34(14). Retrieved from https://doi.org/10.1007/s00521-022-07076-w
  • Lai, K. L., Niddam, D. M., Fuh, J. L., Chen, W. T., Wu, J. C., & Wang, S. J. (2020). Cortical morphological changes in chronic migraine in a Taiwanese cohort: Surface- and voxel-based analyses. Cephalalgia, 40(6). Retrieved from https://doi.org/10.1177/0333102420920005
  • Ledig, C., Schuh, A., Guerrero, R., Heckemann, R. A., & Rueckert, D. (2018). Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Reports 2018 8:1, 8(1), 1–16. Retrieved 31 March 2023 from https://doi.org/10.1038/s41598-018-29295-9
  • Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis. Retrieved from https://doi.org/10.1016/j.media.2017.07.005
  • Manera, A. L., Dadar, M., Collins, D. L., & Ducharme, S. (2019). Deformation based morphometry study of longitudinal MRI changes in behavioral variant frontotemporal dementia. NeuroImage: Clinical, 24. Retrieved from https://doi.org/10.1016/j.nicl.2019.102079
  • Mustafa Abdullah, D., & Mohsin Abdulazeez, A. (2021). Machine Learning Applications based on SVM Classification A Review. Qubahan Academic Journal, 1(2). Retrieved from https://doi.org/10.48161/qaj.v1n2a50
  • Nir, T. M., Villalon-Reina, J. E., Prasad, G., Jahanshad, N., Joshi, S. H., Toga, A. W., … Thompson, P. M. (2015). Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer’s disease. Neurobiology of Aging, 36(S1), S132–S140. Retrieved from https://doi.org/10.1016/J.NEUROBIOLAGING.2014.05.037
  • Othman, N. A., & Aydin, I. (2022). A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies. Traitement Du Signal, 39(6), 1951–1959. Retrieved from https://doi.org/10.18280/TS.390607
  • Pienaar, R. (n.d.). med2image. Retrieved 31 March 2023 from https://github.com/FNNDSC/med2image
  • Plasensia, O. E. . (2019). Personalized Medicine: Comparison of Techniques for the Automatic Diagnosis of Alzheimer’s Disease. Unir la Universidad En Internet.
  • Pacal, I., & Karaboga, D. (2021). A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134, 104519.Retrieved from https://doi.org/10.1016/j.compbiomed.2021.104519
  • Pacal, İ. Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images. Journal of the Institute of Science and Technology, 12(4), 1917-1927. Retrieved from https://doi.org/10.21597/jist.1183679
  • Savaş, S. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218. Retrieved 31 March 2023 from https://doi.org/10.1007/S13369-021-06131-3/TABLES/5
  • Wu, L., Rosa-Neto, P., & Gauthier, S. (2011). Use of biomarkers in clinical trials of alzheimer disease: From concept to application. Molecular Diagnosis and Therapy. Retrieved from https://doi.org/10.2165/11595090-000000000-00000
  • Yaman, O., & Tuncer, T. (2022). Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images. Biomedical Signal Processing and Control, 73. Retrieved from https://doi.org/10.1016/j.bspc.2021.103428
  • Yang, S. T., Lee, J. Der, Huang, C. H., Wang, J. J., Hsu, W. C., & Wai, Y. Y. (2010). Computer-aided diagnosis of Alzheimer’s disease using multiple features with artificial neural network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6230 LNAI, 699–705. Retrieved 31 March 2023 from https://doi.org/10.1007/978-3-642-15246-7_72/COVER
  • 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. Retrieved from https://doi.org/10.1016/J.NEUROSCIENCE.2019.05.014
  • Zhang, J., & Shi, S. (2013). A literature review of AD7c-ntp as a biomarker for Alzheimer’s disease. Annals of Indian Academy of Neurology. Retrieved from https://doi.org/10.4103/0972-2327.116902
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Bilgisayar Mühendisliği / Computer Engineering
Yazarlar

Selahattin Barış Çelebi 0000-0002-6235-9348

Bülent Gürsel Emiroğlu 0000-0002-1656-6450

Erken Görünüm Tarihi 29 Ağustos 2023
Yayımlanma Tarihi 1 Eylül 2023
Gönderilme Tarihi 2 Nisan 2023
Kabul Tarihi 28 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 3

Kaynak Göster

APA Çelebi, S. B., & Emiroğlu, B. G. (2023). Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Journal of the Institute of Science and Technology, 13(3), 1454-1467. https://doi.org/10.21597/jist.1275669
AMA Çelebi SB, Emiroğlu BG. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Iğdır Üniv. Fen Bil Enst. Der. Eylül 2023;13(3):1454-1467. doi:10.21597/jist.1275669
Chicago Çelebi, Selahattin Barış, ve Bülent Gürsel Emiroğlu. “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”. Journal of the Institute of Science and Technology 13, sy. 3 (Eylül 2023): 1454-67. https://doi.org/10.21597/jist.1275669.
EndNote Çelebi SB, Emiroğlu BG (01 Eylül 2023) Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Journal of the Institute of Science and Technology 13 3 1454–1467.
IEEE S. B. Çelebi ve B. G. Emiroğlu, “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”, Iğdır Üniv. Fen Bil Enst. Der., c. 13, sy. 3, ss. 1454–1467, 2023, doi: 10.21597/jist.1275669.
ISNAD Çelebi, Selahattin Barış - Emiroğlu, Bülent Gürsel. “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”. Journal of the Institute of Science and Technology 13/3 (Eylül 2023), 1454-1467. https://doi.org/10.21597/jist.1275669.
JAMA Çelebi SB, Emiroğlu BG. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Iğdır Üniv. Fen Bil Enst. Der. 2023;13:1454–1467.
MLA Çelebi, Selahattin Barış ve Bülent Gürsel Emiroğlu. “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”. Journal of the Institute of Science and Technology, c. 13, sy. 3, 2023, ss. 1454-67, doi:10.21597/jist.1275669.
Vancouver Çelebi SB, Emiroğlu BG. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Iğdır Üniv. Fen Bil Enst. Der. 2023;13(3):1454-67.

Cited By

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting
International Journal of Engineering and Innovative Research
https://doi.org/10.47933/ijeir.1387314
 Kapak Resmi İndir