Abstract
The origin of dementia can be largely attributed to Alzheimer’s disease (AD). The progressive nature of AD causes the brain cell deterioration that eventfully leads to physical dependency and mental disability which hinders a person’s normal life. A computer-aided diagnostic system is required that can aid physicians in diagnosing AD in real-time. The AD stages classification remains an important research area. To extract the deep-features, the traditional machine learning-based and deep learning-based methods often require large dataset and that leads to class imbalance and overfitting issues. To overcome this problem, the use an efficient transfer learning architecture to extract deep features which are further used for AD stage classification. In this study, an Alzheimer’s stage detection system is proposed based on deep features using a pre-trained AlexNet model, by transferring the initial layers from pre-trained AlexNet model and extract the deep features from the Convolutional Neural Network (CNN). For the classification of extracted deep-features, we have used the widely used machine learning algorithms including support vector machine (SVM), k-nearest neighbor (KNN), and Random Forest (RF). The evaluation results of the proposed scheme show that a deep feature-based model outperformed handcrafted and deep learning method with 99.21% accuracy. The proposed model also outperforms existing state-of-the-art methods.
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References
Afzal S, Maqsood M, Nazir F, Khan U, Aadil F, Awan KM, Mehmood I, Song OY (2019) A data augmentation-based framework to handle class imbalance problem for Alzheimer’s stage detection. IEEE Access 7:115528–115539
Ahmed OB et al (2015) Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features. Multimed Tools Appl 74(4):1249–1266
Ahmed OB et al (2015) Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex. Comput Med Imaging Graph 44:13–25
Alkabawi, E.M., A.R. Hilal, and O.A. Basir 2017. Computer-aided classification of multi-types of dementia via convolutional neural networks. In 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE.
Altaf, T., et al. Multi-class Alzheimer disease classification using hybrid features. in IEEE Future Technologies Conference. 2017.
Altaf T, Anwar SM, Gul N, Majeed MN, Majid M (2018) Multi-class Alzheimer's disease classification using image and clinical features. Biomed. Signal Process. Control 43:64–74
Beheshti I, Demirel H (2016) And a.s.D.N. initiative, Feature-ranking-based Alzheimer’s disease classification from structural MRI. Magn Reson Imaging 34(3):252–263
Beheshti I, Demirel H, Matsuda H, Alzheimer's Disease Neuroimaging Initiative (2017) Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 83:109–119
Belleville S et al (2014) Detecting early preclinical Alzheimer's disease via cognition, neuropsychiatry, and neuroimaging: qualitative review and recommendations for testing. J Alzheimers Dis 42(s4):S375–S382
Chincarini A, Bosco P, Calvini P, Gemme G, Esposito M, Olivieri C, Rei L, Squarcia S, Rodriguez G, Bellotti R, Cerello P, de Mitri I, Retico A, Nobili F, Alzheimer's Disease Neuroimaging Initiative (2011) Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease. Neuroimage 58(2):469–480
Chitradevi D, Prabha S (2020) Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl Soft Comput 86:105857
Choi H, Jin KH, A.s.D.N. Initiative (2018) Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res 344:103–109
Deng, J., et al. 2009 Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee.
Farouk, Y., S. Rady, and H. Faheem 2018. Statistical features and voxel-based morphometry for alzheimer's disease classification. In 2018 9th International Conference on Information and Communication Systems (ICICS). IEEE.
Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321–331
Fung G, Stoeckel J (2007) SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information. Knowl Inf Syst 11(2):243–258
Gao XW, Hui R, Tian Z (2017) Classification of CT brain images based on deep learning networks. Comput Methods Prog Biomed 138:49–56
Guerrero R, Wolz R, Rao AW, Rueckert D (2014) Manifold population modeling as a neuro-imaging biomarker: application to ADNI and ADNI-GO. NeuroImage 94:275–286
Guyon, I., et al. 2008, Feature extraction: foundations and applications. Vol. 207: Springer.
Hao X, Bao Y, Guo Y, Yu M, Zhang D, Risacher SL, Saykin AJ, Yao X, Shen L, Alzheimer's Disease Neuroimaging Initiative (2020) Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease. Med Image Anal 60:101625
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621
Islam, J. and Y. Zhang 2017. A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In International Conference on Brain Informatics. Springer.
Klöppel S et al (2008) Automatic classification of MR scans in Alzheimer's disease. Brain 131(3):681–689
Lao Z, Shen D, Xue Z, Karacali B, Resnick SM, Davatzikos C (2004) Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage 21(1):46–57
Liu, Y., et al. 2004.Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer’s disease classification. In International conference on medical image computing and computer-assisted intervention. Springer.
Maqsood M, Nazir F, Khan U, Aadil F, Jamal H, Mehmood I, Song OY (2019) Transfer learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI scans. Sensors 19(11):2645
Mishra S, Majhi B, Sa PK, Sharma L (2017) Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomed Signal Process Control 33:272–280
Nanni L, Salvatore C, Cerasa A, Castiglioni I (2016) Combining multiple approaches for the early diagnosis of Alzheimer's disease. Pattern Recogn Lett 84:259–266
Noothout, J.M., et al. 2018, CNN-based Landmark Detection in Cardiac CTA Scans. arXiv preprint arXiv:1804.04963,.
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 Syst Appl 140:112873
Plocharski M, Østergaard LR, A.s.D.N. Initiative (2016) Extraction of sulcal medial surface and classification of Alzheimer's disease using sulcal features. Comput Methods Prog Biomed 133:35–44
Ramaniharan AK, Manoharan SC, Swaminathan R (2016) Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers. Expert Syst Appl 59:208–216
Sarraf S, Tofighi G (2016) DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. BioRxiv:070441
Shi, Y.Q., H.-J. Kim, and F. Perez-Gonzalez 2012, Digital Forensics and Watermarking: 10th International Workshop, IWDW 2011, Atlantic City, NJ, USA, Oct. 23–26, 2011, Revised Selected Papers. Vol. 7128: Springer.
Shikalgar A, Sonavane S (2020) Hybrid Deep Learning Approach for Classifying Alzheimer Disease Based on Multimodal Data. In: Computing in Engineering and Technology. Springer, pp 511–520
Wang S, Zhang Y, Liu G, Phillips P, Yuan TF (2016) Detection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimers Dis 50(1):233–248
Westman E, Cavallin L, Muehlboeck JS, Zhang Y, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Spenger C, Lovestone S, Simmons A, Wahlund LO, for the AddNeuroMed consortium (2011) Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer's disease. PLoS One 6(7):e22506
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668).
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Nawaz, H., Maqsood, M., Afzal, S. et al. A deep feature-based real-time system for Alzheimer disease stage detection. Multimed Tools Appl 80, 35789–35807 (2021). https://doi.org/10.1007/s11042-020-09087-y
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DOI: https://doi.org/10.1007/s11042-020-09087-y