Advertisement

Accuracy-Based Performance Analysis of Alzheimer’s Disease Classification Using Deep Convolution Neural Network

  • Ketki C. PathakEmail author
  • Swathi S. Kundaram
Conference paper
  • 21 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)

Abstract

Many researchers have utilized various statistical and machine learning models for detection of Alzheimer’s disease. There is been a common practice of analyzing magnetic resonance imaging (MRI) for Alzheimer’s disease diagnosis in clinical research. Based on similarity between healthy and demented MRI data of older people has been done for Alzheimer’s disease detection. Recently, advanced deep learning techniques have successfully illustrated human-level performance in various fields, including medical image analysis, which is advantageous over hand crafted feature extraction methods. Convolutional neural network (CNN) provided better potential for accuracy in diagnosis the Alzheimer’s disease such as to classify the given input as cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer disease (AD). In this work, we have presented a framework based on DCNN for Alzheimer’s disease detection in terms of accuracy. We have achieved 97.98% accuracy on our dataset without using any handcrafted features for training the network. Validation accuracy achieved is 91.75%. Experimental data is obtained from ADNI and total 13,733 images from 266 subjects are used.

Keywords

Convolution neural network Accuracy Alzheimer’s disease Deep learning Classification 

References

  1. 1.
    Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J.: Applications of deep learning to MRI images: a survey. Big Data Min. Anal. 1(1), 1–18 (2018)CrossRefGoogle Scholar
  2. 2.
    Alzheimer’s Association. Alzheimer’s disease facts and figures. Alzheimer’s Dementia 12(4), 459–509 (2016)Google Scholar
  3. 3.
    Alzheimer’s Association.: Alzheimer’s disease facts and figures. Alzheimer’s Dementia 14(3), 367–429 (2018)Google Scholar
  4. 4.
    Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2(4), 189–210 (1994)CrossRefGoogle Scholar
  5. 5.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: IEEE Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)Google Scholar
  6. 6.
    de Bruijne, M.: Machine learning approaches in medical image analysis: from detection to diagnosis. J. Med. Image Anal. 33, 94–97 (2016)Google Scholar
  7. 7.
    Rathore, S., Habes, M., Iftikhar, M.A., Shacklett, A., Davatzikos, C.: A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage 155, 530–548 (2017)CrossRefGoogle Scholar
  8. 8.
    Ortiz, A., Munilla, J., Gorriz, J.M., Ramirez, J.: Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int. J. Neural Syst. 26(07), 1650025 (2016)CrossRefGoogle Scholar
  9. 9.
    Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2014)CrossRefGoogle Scholar
  10. 10.
    Islam, J., Zhang, Y.: Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inf. 5(2), 2 (2018)CrossRefGoogle Scholar
  11. 11.
    Liu, M., Cheng, D., Yan, W., Alzheimer’s Disease Neuroimaging Initiative.: Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Frontiers Neuroinf. 12, 35 (2018)Google Scholar
  12. 12.
    Cui, R., Liu, M., Li, G.: Longitudinal analysis for Alzheimer’s disease diagnosis using RNN. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1398–1401 (2018)Google Scholar
  13. 13.
    Gunawardena, K.A.N.N.P., Rajapakse, R.N., Kodikara, N.D.: Applying convolutional neural networks for pre-detection of Alzheimer’s disease from structural MRI data. In: 2017 24th IEEE International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1–7 (2017)Google Scholar
  14. 14.
    Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A., Catheline, G.: 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. arXiv:1801.05968 (2018)
  15. 15.
    Wang, S.H., Phillips, P., Sui, Y., Liu, B., Yang, M., Cheng, H.: Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42(5), 85 (2018)CrossRefGoogle Scholar
  16. 16.
    ADNI—Alzheimer’s Disease Neuroimaging Initiative. Available http://adni.loni.usc.edu/
  17. 17.
    Horizon Radio Imaging Centre. Surat, Gujarat, IndiaGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Sarvajanik College of Engineering and TechnologySuratIndia

Personalised recommendations