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MR GÖRÜNTÜLERİNDEN ALZHEİMER TESPİTİNDE BOYUT AZALTMA VE DERİN ÖĞRENME YAKLAŞIMLARININ KARŞILAŞTIRILMASI

Year 2022, Volume: 13 Issue: 3, 485 - 491, 30.09.2022
https://doi.org/10.24012/dumf.1141233

Abstract

Her yıl milyonlarca insana Alzheimer teşhisi konulmaktadır. Alzheimer, nörodejeneratif bir hastalıktır. Kliniklerde bu hastalığın en doğru tespiti için biyopsi işlemi uygulanmaktadır. Ancak bu işlem beyin üzerinden gerçekleştirildiğinden hasta için büyük bir risk teşkil etmektedir. Bundan dolayı bu tür hastalıkların tespit edilmesinde daha çok nörogörünütleme teknikleri tercih edilmektedir. Bu nörogörünteleme tekniklerinden biri de Manyetik Rezonans (MR) görüntülemedir. MR invazif olmayan bir araçtır. Bundan dolayı kliniklerde çokça tercih edilmektedir. Bunun yanında mühendislik alanında MR görüntüleri kullanılarak bilgisayar destekli tanı sistemleri de geliştirilmektedir. Bu çalışmada dört farklı Alzheimer sınıfı içeren MR görüntüleri kullanılarak, bu hastalığın demans seviyesi tespit edilmeye çalışılmıştır. Veri seti; orta demans, hafif demans, çok hafif demans ve demans olmayan sınıflardan oluşmaktadır. Çalışmada ilk önce, MR görüntüleri ham olarak matrislere dönüştürülmüştür. Elde edilen matrislere dağılımın normale yaklaştığı, standart sapmanın bir değerini aldığı standardizasyon işlemi uygulanmıştır. Daha sonra veri seti Evrişimsel Sinir Ağında (ESA) sınıflandırılmıştır. Aynı zamanda Temel Bileşen Analizi (TBA), Bağımsız Bileşen Analizi (BBA) ve Yerel Doğrusal Gömme (YDG) yöntemleri ayrı ayrı uygulanarak, öznitelik vektörü elde edilmiştir. Elde edilen öznitelik vektörü k-NN sınıflandırıcı ile sınıflandırılmıştır. Sınıflandırma sonucunda ESA, k-NN-TBA, k-NN-BBA ve k-NN-YDG yöntemlerinde sırasıyla, %88.44, %95.52, %98.22 ve %91.14 sınıflandırma doğruluğu bulunmuştur. Çalışma sonucunda en iyi performansın BBA tabanlı k-NN sınıflandırıcı ile elde edildiği görülmüştür.

References

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Year 2022, Volume: 13 Issue: 3, 485 - 491, 30.09.2022
https://doi.org/10.24012/dumf.1141233

Abstract

References

  • [1] Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. F., & Marchiori, E. (2022). Deep learning for Alzheimer's disease diagnosis: A survey. Artificial Intelligence in Medicine, 102332.
  • [2] Alzheimer's Association. (2019). 2019 Alzheimer's disease facts and figures. Alzheimer's & dementia, 15(3), 321-387.
  • [3] Shanmugam, J. V., Duraisamy, B., Simon, B. C., & Bhaskaran, P. (2022). Alzheimer’s disease classification using pre-trained deep networks. Biomedical Signal Processing and Control, 71, 103217.
  • [4] Wadhwa, A., Bhardwaj, A., & Verma, V. S. (2019). A review on brain tumor segmentation of MRI images. Magnetic resonance imaging, 61, 247-259.
  • [5] Park, C., Ha, J., & Park, S. (2020). Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset. Expert Systems with Applications, 140, 112873.
  • [6] Liu, C. F., Padhy, S., Ramachandran, S., Wang, V. X., Efimov, A., Bernal, A., ... & Alzheimer's Disease Neuroimaging Initiative. (2019). Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's disease and mild cognitive impairment. Magnetic resonance imaging, 64, 190-199.
  • [7] Lee, E., Choi, J. S., Kim, M., Suk, H. I., & Alzheimer’s Disease Neuroimaging Initiative. (2019). Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning. Neuroimage, 202, 116113.
  • [8] Goenka, N., & Tiwari, S. (2022). AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches. Biomedical Signal Processing and Control, 74, 103500.
  • [9] Öziç, M. Ü., & Özşen, S. (2020). 3B alzheimer MR görüntülerinin hacimsel kayıp bölgelerindeki voksel değerleri kullanılarak sınıflandırılması. El-Cezeri Journal of Science and Engineering, 7(3), 1152-1166.
  • [10] Oh, K., Chung, Y. C., Kim, K. W., Kim, W. S., & Oh, I. S. (2019). Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Scientific Reports, 9(1), 1-16.
  • [11] Rieke, J., Eitel, F., Weygandt, M., Haynes, J. D., & Ritter, K. (2018). Visualizing convolutional networks for MRI-based diagnosis of Alzheimer’s disease. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications (pp. 24-31). Springer, Cham.
  • [12] https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
  • [13] Mita, J. H., Babu, C. G., & Shankar, M. G. (2021, March). Performance analysis of dimensionality reduction using PCA, KPCA and LLE for ECG signals. In IOP Conference Series: Materials Science and Engineering (Vol. 1084, No. 1, p. 012005). IOP Publishing
  • [14] Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
  • [15] Liu, X., & Zhao, C. (2022). Research on Image Feature Extraction Algorithm of the Egg and Egg White Protein Thermal Gelation Based on PCA/ICA. Computational Intelligence and Neuroscience, 2022.
  • [16] Hyvärinen, A. (1999). Survey on independent component analysis.
  • [17] Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500), 2323-2326. [18] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
  • [19] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET) (pp. 1-6). Ieee.
  • [20] Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.
  • [21] Liao, Y., & Vemuri, V. R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & security, 21(5), 439-448.
  • [22] Gupta, A., Ayhan, M., & Maida, A. (2013, May). Natural image bases to represent neuroimaging data. In International conference on machine learning (pp. 987-994). PMLR.
  • [23] Jain, R., Jain, N., Aggarwal, A., & Hemanth, D. J. (2019). Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research, 57, 147-159.
  • [24] Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Shen, L., ... & Alzheimer’s Disease Neuroimaging Initiative. (2020). A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage, 208, 116459.
  • [25] Goenka, N., & Tiwari, S. (2022). AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches. Biomedical Signal Processing and Control, 74, 103500.
There are 24 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Ömer Türk 0000-0002-0060-1880

Early Pub Date September 30, 2022
Publication Date September 30, 2022
Submission Date July 6, 2022
Published in Issue Year 2022 Volume: 13 Issue: 3

Cite

IEEE Ö. Türk, “MR GÖRÜNTÜLERİNDEN ALZHEİMER TESPİTİNDE BOYUT AZALTMA VE DERİN ÖĞRENME YAKLAŞIMLARININ KARŞILAŞTIRILMASI”, DUJE, vol. 13, no. 3, pp. 485–491, 2022, doi: 10.24012/dumf.1141233.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456