Abstract
Alzheimer’s disease is becoming common in the world with the time. It is an irreversible and progressive brain disorder that slowly destroys the memory and thinking skills and, eventually, the ability to perform the simplest tasks. It becomes severe before the noticeable symptoms appear and causes brain disorder which cannot be cured by any medicines and therapies, however its progression can be slow down through early diagnosis. In this paper, we employed different CNN based transfer learning methods for Alzheimer disease classification. We have applied different parameters, and achieved remarkable accuracy on benchmark ADNI dataset. We have tested 13 differnt flavours of different pre-trained CNN models using a fine-tuned approach of transfer learning across two different domain on ADNI dataset (94 AD, 138 MCI and 146 NC). Comparatively, DenseNet showed better performance by achieving a maximal average accuracy of % 99.05. Significant improvement in accuracy has been observed as compared to previously reported works in terms of specificity, sensitivity and accuracy. The source code of propose framework is publicly available.
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References
Anand KS, Dhikav V (2012) Hippocampus in health and disease: an overview. Ann Indian Acad Neurol 15(4):239
Bäckström K, Nazari M, Gu IYH, Jakola AS (2018) An efficient 3d deep convolutional network for alzheimer’s disease diagnosis using mr images. In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018). IEEE, pp 149–153
Ebrahimi-Ghahnavieh A, Luo S, Chiong R (2019) Transfer learning for alzheimer’s disease detection on mri images. In: 2019 IEEE International conference on industry 4.0, artificial intelligence, and communications technology (IAICT). IEEE, pp 133–138
Emmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M (2020) An introductory review of deep learning for prediction models with big data. Front Artif Intell 3:4. https://doi.org/https://www.frontiersin.org/article/10.3389/frai.2020.00004, https://doi.org/10.3389/frai.2020.00004
Farooq A, Anwar S, Awais M, Rehman S (2017) A deep cnn based multi-class classification of alzheimer’s disease using mri. In: 2017 IEEE International conference on imaging systems and techniques (IST). IEEE, pp 1–6
Gautam C, Mishra PK, Tiwari A, Richhariya B, Pandey HM, Wang S et al (2020) Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data. Neur Netw 123:191–216. http://www.sciencedirect.com/science/article/pii/S0893608019303934. https://doi.org/10.1016/j.neunet.2019.12.001
Gerstner W, Kempter R, van Hemmen JL, Wagner H (1999) Pulsed neural networks. chap. Hebbian learning of pulse timing in the barn owl auditory system. Cambridge, MIT Press. ISBN 0-626-13350-4; pp 353–377. http://dl.acm.org/citation.cfm?id=296533.296554
Gupta A, Ayhan MS, Maida AS (2013) Natural image bases to represent neuroimaging data. In: Proceedings of the 30th international conference on international conference on machine learning - volume 28; ICML’13. JMLR.org, pp III–987–III–994. http://dl.acm.org/citation.cfm?id=3042817.3043047
Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC (2014) An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psych 85(6):692–698. https://doi.org/10.1136/jnnp-2013-306285
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR). pp 770–778
Hosseini-Asl E, Gimel’farb G, El-Baz A (2016) Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network, arXiv:1607.00556
Hosseini-Asl E, Keynton R, El-Baz A (2016) Alzheimer’s disease diagnostics by adaptation of 3d convolutional network. In: 2016 IEEE International conference on image processing (ICIP). IEEE, pp 126–130
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:170404861
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Hunsberger E, Eliasmith C (2015) Spiking deep networks with LIF neurons, arXiv:1510.08829
Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: alexnet-level accuracy with 50x fewer parameters and < 0.5mb model size. arXiv:1602.07360
Iyer LR, Chua Y, Li H (2018) Is neuromorphic mnist neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain, arXiv:1807.01013
Ju R, Hu C, Zhou P, Li Q (2019) Early diagnosis of alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinforma (TCBB) 16(1):244–257
Kazemi Y, Houghten S (2018) A deep learning pipeline to classify different stages of alzheimer’s disease from fmri data. In: 2018 IEEE Conference on computational intelligence in bioinformatics and computational biology (CIBCB). IEEE, pp 1–8
Khan RU, Tanveer M, Pachori RB, (ADNI) ADNI (2020) A novel method for the classification of alzheimer’s disease from normal controls using magnetic resonance imaging. Expert Syst, 1–22
Liu M, Li F, Yan H, Wang K, Ma Y, Shen L et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in alzheimer’s disease. Neuro Image 208:116459. http://www.sciencedirect.com/science/article/pii/S105381191931050X, https://doi.org/10.1016/j.neuroimage.2019.116459
Mathew J, Mekkayil L, Ramasangu H, Karthikeyan BR, Manjunath AG (2016) Robust algorithm for early detection of alzheimer’s disease using multiple feature extractions. In: 2016 IEEE Annual India conference (INDICON). IEEE, pp 1–6
Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Applic 32(3):839–854
Razzak MI, Imran M, Xu G (2018) Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inform 23(5):1911–1919
Razzak MI, Imran M, Xu G (2020) Big data analytics for preventive medicine. Neural Comput Applic 32(9):4417–4451
Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. In: Classification in BioApps. Springer, pp 323–350
Rehman A, Naz S, Razzak I (2020) Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. arXiv:200409010
Rehman A, Naz S, Razzak MI, Akram F, Imran M (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ Syst Signal Process 39(2):757–775
Sandeep C, Kumar AS, Susanth M (2017) The online datasets used to classify the different stages for the early diagnosis of alzheimer’s disease (ad). Int J Eng Adv Technol 6(4):38–45
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520
Sarraf S, Tofighi G et al (2016) Deepad: Alzheimer’s disease classification via deep convolutional neural networks using mri and fmri. arXiv:070441
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556
Sutskever I, Hinton GE, Krizhevsky A (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 1097–1105
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4 inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI conference on artificial intelligence
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, et al. (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imag 35(5):1299–1312
Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, et al. (2020) Machine learning techniques for the diagnosis of alzheimer’s disease: a review. ACM Trans Multimed Comput Commun Appl, 16(1). https://doi.org/10.1145/3344998
Wang S, Wang H, Shen Y, Wang X (2018) Automatic recognition of mild cognitive impairment and alzheimers disease using ensemble based 3d densely connected convolutional networks. In: 2018 17th IEEE International conference on machine learning and applications (ICMLA), pp 517–523. https://doi.org/10.1109/ICMLA.2018.00083
Yaqoob M, Wróbel B (2017) Very small spiking neural networks evolved to recognize a pattern in a continuous input stream. In: 2017 IEEE Symposium series on computational intelligence (SSCI), p 1–8. https://doi.org/10.1109/SSCI.2017.8285420
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Ashraf, A., Naz, S., Shirazi, S.H. et al. Deep transfer learning for alzheimer neurological disorder detection. Multimed Tools Appl 80, 30117–30142 (2021). https://doi.org/10.1007/s11042-020-10331-8
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DOI: https://doi.org/10.1007/s11042-020-10331-8