Araştırma Makalesi
BibTex RIS Kaynak Göster

Classification and Segmentation of Alzheimer Disease in MRI Modality using the Deep Convolutional Neural Networks

Yıl 2022, Sayı: 40, 99 - 105, 30.09.2022
https://doi.org/10.31590/ejosat.1171810

Öz

In the study, classification and segmentation tasks were implemented for analysis of Alzheimer's disease. In classification task, 7 different models were tested using transfer learning. The GoogLeNet model achieved the best classification performance with the accuracy of 0.9467, sensitivity of 0.9474, specificity of 0.9811, and F1-score of 0.9467. In segmentation task, U-Net architecture design was used for the segmentation of Alzheimer's disease. U-Net model achieved the dice of 0.874, IoU of 0.776, sensitivity of 0.868, specificity of 0.999, precision of 0.879, and accuracy of 0.999. In order to create the pipeline, classification and segmentation models were used together. Consequently, a computer vision-assisted decision support system was created.

Teşekkür

This paper has been prepared by AKGUN Computer Incorporated Company. We would like to thank AKGUN Computer Inc. for providing all kinds of opportunities and funds for the execution of this project.

Kaynakça

  • Alp Eren, H., Okyay, S., Adar, N., Üniversitesi, E. O., Fakültesi, M.-M., Bölümü, M., Anahtar, T., & Öz, K. (2021). ADOKEN: MR İÇİN DERİN ÖĞRENME TABANLI KARAR DESTEK YAZILIMI. Journal of Engineering Sciences and Design, 9(2), 406–413. https://doi.org/10.21923/JESD.887327
  • Öziç, M. Ü., & Özşen, S. (2020). Classification of 3b alzheimer’s mr images using voxel values in volumetric loss regions. El-Cezeri Journal of Science and Engineering, 7(3), 1152–1166. https://doi.org/10.31202/ecjse.728049
  • John, R., & Kunju, N. (2018). Detection of Alzheimer’s Disease Using Fractional Edge Detection. Journal of Biodiversity & Endangered Species, 09(03). https://doi.org/10.4172/2229-8711.1000230
  • Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A., & Catheline, G. (2018). 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. https://ida.loni.usc.edu
  • Hong, X., Lin, R., Yang, C., Zeng, N., Cai, C., Gou, J., & Yang, J. (2019). Predicting Alzheimer’s Disease Using LSTM. IEEE Access, 7, 80893–80901. https://doi.org/10.1109/ACCESS.2019.2919385
  • Allioui, H., Sadgal, M., & Elfazziki, A. (2019). Deep MRI Segmentation: A Convolutional Method Applied to Alzheimer Disease Detection. IJACSA) International Journal of Advanced Computer Science and Applications, 10(11). www.ijacsa.thesai.org
  • Ahmed, S., Choi, K. Y., Lee, J. J., Kim, B. C., Kwon, G. R., Lee, K. H., & Jung, H. Y. (2019). Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases. IEEE Access, 7, 73373–73383. https://doi.org/10.1109/ACCESS.2019.2920011
  • Vieira, S., Pinaya, W. H. L., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58–75. https://doi.org/10.1016/J.NEUBIOREV.2017.01.002
  • SARVESH DUBEY. (2020). Alzheimer’s Dataset ( 4 class of Images) Images of MRI Segementation. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
  • ADNI | Alzheimer’s Disease Neuroimaging Initiative. (2003). https://adni.loni.usc.edu/
  • Weng, W., & Zhu, X. (2021). INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access, 9, 16591–16603. https://doi.org/10.1109/ACCESS.2021.3053408
  • Iqbal, H. (2018). Harisiqbal88/plotneuralnet v1. 0.0. URL: https://doi. org/10.5281/Zenodo.

Derin Evrişimli Sinir Ağlarını Kullanarak MRG Modalitesinde Alzheimer Hastalığının Sınıflandırılması ve Segmentasyonu

Yıl 2022, Sayı: 40, 99 - 105, 30.09.2022
https://doi.org/10.31590/ejosat.1171810

Öz

Çalışmada Alzheimer hastalığının analizi için sınıflandırma ve segmentasyon görevleri uygulanmıştır. Sınıflandırma görevinde transfer öğrenme kullanılarak 7 farklı model test edilmiştir. GoogLeNet modeli 0.9467 doğruluk, 0.9474 duyarlılık, 0.9811 özgüllük ve 0.9467 F1 skoru ile en iyi sınıflandırma performansını elde etmiştir. Segmentasyon görevinde, Alzheimer hastalığının segmentasyonu için U-Net mimari tasarımı kullanılmıştır. U-Net modeli 0.874 zar skoru, 0.776 IoU, 0.868 duyarlılık, 0.999 özgüllük, 0.879 kesinlik ve 0.999 doğruluk elde etmiştir. Pipeline oluşturmak için sınıflandırma ve segmentasyon modelleri birlikte kullanılmıştır. Sonuç olarak, bilgisayarlı görü destekli bir karar destek sistemi oluşturulmuştur.

Kaynakça

  • Alp Eren, H., Okyay, S., Adar, N., Üniversitesi, E. O., Fakültesi, M.-M., Bölümü, M., Anahtar, T., & Öz, K. (2021). ADOKEN: MR İÇİN DERİN ÖĞRENME TABANLI KARAR DESTEK YAZILIMI. Journal of Engineering Sciences and Design, 9(2), 406–413. https://doi.org/10.21923/JESD.887327
  • Öziç, M. Ü., & Özşen, S. (2020). Classification of 3b alzheimer’s mr images using voxel values in volumetric loss regions. El-Cezeri Journal of Science and Engineering, 7(3), 1152–1166. https://doi.org/10.31202/ecjse.728049
  • John, R., & Kunju, N. (2018). Detection of Alzheimer’s Disease Using Fractional Edge Detection. Journal of Biodiversity & Endangered Species, 09(03). https://doi.org/10.4172/2229-8711.1000230
  • Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A., & Catheline, G. (2018). 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. https://ida.loni.usc.edu
  • Hong, X., Lin, R., Yang, C., Zeng, N., Cai, C., Gou, J., & Yang, J. (2019). Predicting Alzheimer’s Disease Using LSTM. IEEE Access, 7, 80893–80901. https://doi.org/10.1109/ACCESS.2019.2919385
  • Allioui, H., Sadgal, M., & Elfazziki, A. (2019). Deep MRI Segmentation: A Convolutional Method Applied to Alzheimer Disease Detection. IJACSA) International Journal of Advanced Computer Science and Applications, 10(11). www.ijacsa.thesai.org
  • Ahmed, S., Choi, K. Y., Lee, J. J., Kim, B. C., Kwon, G. R., Lee, K. H., & Jung, H. Y. (2019). Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases. IEEE Access, 7, 73373–73383. https://doi.org/10.1109/ACCESS.2019.2920011
  • Vieira, S., Pinaya, W. H. L., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58–75. https://doi.org/10.1016/J.NEUBIOREV.2017.01.002
  • SARVESH DUBEY. (2020). Alzheimer’s Dataset ( 4 class of Images) Images of MRI Segementation. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
  • ADNI | Alzheimer’s Disease Neuroimaging Initiative. (2003). https://adni.loni.usc.edu/
  • Weng, W., & Zhu, X. (2021). INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access, 9, 16591–16603. https://doi.org/10.1109/ACCESS.2021.3053408
  • Iqbal, H. (2018). Harisiqbal88/plotneuralnet v1. 0.0. URL: https://doi. org/10.5281/Zenodo.
Toplam 12 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Furkan Karakaya 0000-0003-1103-0208

Caglar Gurkan 0000-0002-4652-3363

Abdulkadir Budak 0000-0002-0328-6783

Hakan Karataş 0000-0002-9497-5444

Erken Görünüm Tarihi 26 Eylül 2022
Yayımlanma Tarihi 30 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 40

Kaynak Göster

APA Karakaya, F., Gurkan, C., Budak, A., Karataş, H. (2022). Classification and Segmentation of Alzheimer Disease in MRI Modality using the Deep Convolutional Neural Networks. Avrupa Bilim Ve Teknoloji Dergisi(40), 99-105. https://doi.org/10.31590/ejosat.1171810