Abstract
Machine learning and deep learning play a crucial role in identification of various diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can be performed promising for prediction of Alzheimer’s disease using MRI scans. Alzheimer disease becoming more common in the people (age 65 years or above). The disease becomes severe before the symptoms appear and causes brain disorder that cannot be cured by medicines and other therapies and treatments. So the early diagnosis is necessary to slow down its progression. Detection and prevention of Alzheimer disease is one of the active research area for the researchers nowadays. In this paper, we employed architectures of convoutional networks using freeze features extracted from source data set ImageNet for binary and ternary classification. All experiments were carried out using Alzheimer’s disease national initiative (ADNI) data set consisting of MRI scans. The performance of proposed system demonstrates for classification of Alzheimer’s disease versus mild cognitive impairment, normal controls versus mild cognitive impairment, and cognitive normal versus Alzheimer’s disease. The results of proposed study show that VGG architecture outperforms the state-of-the-art techniques and number of architectures of conveNet (AlexNet, GoogLeNet, ResNet, DenseNet, Inceptionv3, InceptionResNet) in Alzheimer’s disease detection, and achieves an identification test set accuracy of 99.27% (MCI/AD), 98.89% (AD/CN) and 97.06% (MCI/CN).



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James, B.D., Leurgans, S.E., Hebert, L.E., Scherr, P.A., Yaffe, K., Bennett, D.A.: Contribution of Alzheimer disease to mortality in the united states. Neurology 82(12), 1045–1050 (2014)
Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement. 3(3), 186–191 (2007)
Thakare P., Pawar V.: Alzheimer disease detection and tracking of Alzheimer patient. In: 2016 International Conference on Inventive Computation Technologies (ICICT); vol. 1. IEEE; 2016, p. 1–4
Abdalla, B., Yassin, M., Abir, M., Bisharat, B., Armaly, Z.: Traditional and modern medicine harmonizing the two approaches in the treatment of neurodegeneration (Alzheimer’s disease - ad). In: Saad, M., de Medeiros, R. (eds.) Complementary Therapies for the Contemporary Healthcare, chap. 10. IntechOpen, Rijeka (2012). https://doi.org/10.5772/48558
Jin, J.: Alzheimer disease. JAMA 313(14), 1488–1488 (2015)
Zahoor, S., Naz, S., Khan, N.H., Razzak, M.I.: Deep optical character recognition: a case of Pashto language. J. Electron. Imaging 29(2), 023002 (2020)
Naz, S., Khan, N.H., Zahoor, S., Razzak, M.I.: Deep OCR for Arabic script-based language like Pastho. Expert Syst. 37(5), e12565 (2020)
Rehman, A., Naz, S., Razzak, M.I., Hameed, I.A.: Automatic visual features for writer identification: a deep learning approach. IEEE Access 7, 17149–17157 (2019)
Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., et al.: Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features. Neural Comput. Appl. 26(8), 219–231 (2015)
Naz, S., Umar, A.I., Ahmed, R.A.S.B., Siddiqi, I., Razzak, M.I.: Offline cursive Nastaliq script recognition using multidimensional recurrent neural networks with statistical features. Neurocomputing 177, 228–241 (2016)
Naseer, A., Rani, M., Naz, S., Razzak, M.I., Imran, M., Xu, G.: Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput. Appl. 32(3), 839–854 (2020)
Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M.: A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ. Syst. Signal Process. 39(2), 757–775 (2020)
Suk, H.I., Lee, S.W., Shen, D., Initiative, A.D.N., et al.: Hierarchical feature representation and multimodal fusion with deep learning for ad/mci diagnosis. NeuroImage 101, 569–582 (2014)
Sarraf S., Tofighi G., et al. Deepad: Alzheimer s disease classification via deep convolutional neural networks using mri and fmri. BioRxiv 070441 (2016)
Mathew, J., Mekkayil, L., Ramasangu, H., Karthikeyan, B.R., Manjunath, A.G.: Robust algorithm for early detection of alzheimer’s disease using multiple feature extractions. In: IEEE Annual India Conference (INDICON). IEEE 2016, 1–6 (2016)
Iftikhar M.A., Idris A.: An ensemble classification approach for automated diagnosis of Alzheimer’s disease and mild cognitive impairment. In: 2016 International Conference on Open Source Systems & Technologies (ICOSST). IEEE; p. 78–83 (2016)
Hosseini-Asl E., Keynton R., El-Baz A.: Alzheimer’s disease diagnostics by adaptation of 3d convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE; p. 126–130 (2016)
Ju, R., Hu, C., Zhou, P., Li, Q.: Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 16(1), 244–257 (2019)
Farooq A., Anwar S., Awais M., Rehman S.: 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; p. 1–6 (2017)
Bäckström, K., Nazari, M., Gu, I.Y.H., Jakola, A.S.: An efficient 3d deep convolutional network for Alzheimer’s disease diagnosis using MR images. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE 2018, 149–153 (2018)
Kazemi Y., Houghten S.: 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; 2018, p. 1–8 (2018)
Qiu, S., Chang, G.H., Panagia, M., Gopal, D.M., Au, R., Kolachalama, V.B.: Fusion of deep learning models of MRI scans, mini-mental state examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 10, 737–749 (2018)
Lin, W., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., et al.: Convolutional neural networks-based MRI image analysis for the Alzheimer’ disease prediction from mild cognitive impairment. Front. Neurosci. 12, 777 (2018)
Payan A., Montana G.: Predicting Alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:150202506 (2015)
Xia, Z., Yue, G., Xu, Y., Feng, C., Yang, M., Wang, T.: A novel end-to-end hybrid network for Alzheimer’ disease detection using 3d CNN and 3d CLSTM. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE 2020, 1–4 (2020)
Ebrahimi-Ghahnavieh A., Luo S., Chiong R.: 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; 2019, p. 133–138 (2019)
Ashraf, A., Naz, S., Shirazi, S.H., Razzak, I., Parsad, M.: Deep transfer learning for Alzheimer neurological disorder detection. Multimed. Tools Appl., 1–26 (2021)
Mehmood, A., Ahmad, A.S., Maqsood, M., Yaqub, M., et al.: A transfer learning approach for early diagnosis of Alzheimer’ disease on MRI images. Neuroscience. 460, 43–52 (2021)
Liu, J., Li, M., Luo, Y., Yang, S., Li, W., Bi, Y.: Alzheimer’s disease detection using depthwise separable convolutional neural networks. Comput. Methods Programs Biomed. 203, 106032 (2021)
Chen, Y., Xia, Y.: Iterative sparse and deep learning for accurate diagnosis of Alzheimer’s disease. Pattern Recognit., 107944 (2021)
Sandeep, C., Kumar, A.S., Susanth, M.: The online datasets used to classify the different stages for the early diagnosis of Alzheimer’ disease (ad). Int. J. Eng. Adv. Technol. 6(4), 38–45 (2017)
Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(4), 1–17 (2020)
Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3d room layout estimation from a single RGB image. IEEE Trans. Multimed. 22(11), 3014–3024 (2020b)
Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43 (2020)
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Naz, S., Ashraf, A. & Zaib, A. Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimedia Systems 28, 85–94 (2022). https://doi.org/10.1007/s00530-021-00797-3
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DOI: https://doi.org/10.1007/s00530-021-00797-3