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Performance Evaluation of Machine and Deep Transfer Learning Techniques for the Classification of Alzheimer Disease Using MRI Images

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Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022) (ICIVC 2022)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 17))

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Abstract

Alzheimer is a neurological disorder that causes cell damage and memory loss. The late symptoms of Alzheimer disease necessitate early identification to minimise the progression of disease and mortality rate. Machine learning and deep learning techniques are required to thoroughly read the MRI images and extract the most relevant information about disease progression. Transfer learning approaches assist in the reduction of computational requirements and minimise the overfitting issues. This study identifies the most appropriate technique for the classification of Alzheimer's disease using machine learning, deep learning, and transfer learning techniques. The classification is performed on the ADNI MRI dataset with the minimal pre-processing of data to compare the results on the same scale. Results suggested that transfer learning approaches outperformed the other algorithms and can be used with other feature extraction techniques to further improve model performance.

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References

  1. Association, A.: 2018 Alzheimer’s disease facts and figures. Alzheimers Dement. 14(3), 367–429 (2018)

    Article  Google Scholar 

  2. Geerlings, M.I., den Heijer, T., Koudstaal, P.J., Hofman, A., Breteler, M.M.B.: History of depression, depressive symptoms, and medial temporal lobe atrophy and the risk of Alzheimer disease. Neurology 70(15), 1258–1264 (2008)

    Article  Google Scholar 

  3. Guillozet, A.L., Weintraub, S., Mash, D.C., Mesulam, M.M.: Neurofibrillary tangles, amyloid, and memory in aging and mild cognitive impairment. Arch. Neurol. 60(5), 729–736 (2003)

    Article  Google Scholar 

  4. Reitz, C., Mayeux, R.: Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem. Pharmacol. 88(4), 640–651 (2014)

    Article  Google Scholar 

  5. Dickerson, B.C., Sperling, R.A.: Large-scale functional brain network abnormalities in Alzheimer’s disease: insights from functional neuroimaging. Behav. Neurol. 21(12), 63–75 (2009)

    Article  Google Scholar 

  6. Simpson, S.L., Bowman, F.D., Laurienti, P.J.: Analyzing complex functional brain networks: fusing statistics and network science to understand the brain. Statist. Surv. 7, 1 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Braun, U., et al.: Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl. Acad. Sci. 112(37), 11678–11683 (2015)

    Article  Google Scholar 

  8. Marsh, H.W., Morin, A.J., Parker, P.D., Kaur, G.: Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. Annu. Rev. Clin. Psychol. 10, 85–110 (2014)

    Article  Google Scholar 

  9. Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017)

    Article  Google Scholar 

  10. Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: overview, challenges and the future. In: Dey, N., Ashour, A.S., Borra, S. (eds.) Classification in BioApps. LNCVB, vol. 26, pp. 323–350. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65981-7_12

    Chapter  Google Scholar 

  11. Rauschert, S., Raubenheimer, K., Melton, P.E., Huang, R.C.: Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification. Clin. Epigenetics 12(1), 1–11 (2020)

    Article  Google Scholar 

  12. Abuhmed, T., El-Sappagh, S., Alonso, J.M.: Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl.-Based Syst. 213, 106688 (2021)

    Article  Google Scholar 

  13. Battineni, G., Chintalapudi, N., Amenta, F.: Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Inf. Med. Unlocked 16, 100200 (2019)

    Article  Google Scholar 

  14. Jha, D., Kwon, G.R.: Alzheimer disease detection in MRI using curvelet transform with KNN. J. Korean Inst. Inf. Technol. 14(8), 121 (2016)

    Google Scholar 

  15. Alickovic, E., Subasi, A.: Automatic detection of alzheimer disease based on histogram and random forest. In: Badnjevic, A., Škrbić, R., Pokvić, L.G. (eds.) CMBEBIH 2019. IP, vol. 73, pp. 91–96. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17971-7_14

    Chapter  Google Scholar 

  16. Dana, A.D., Alashqur, A.: Using decision tree classification to assist in the prediction of Alzheimer's disease. In: 2014 6th international conference on computer science and information technology (CSIT), pp. 122–126. IEEE (2014)

    Google Scholar 

  17. Mehmood, A., Maqsood, M., Bashir, M., Shuyuan, Y.: A deep siamese convolution neural network for multi-class classification of alzheimer disease. Brain Sci. 10(2), 84 (2020)

    Article  Google Scholar 

  18. Álvarez, I., et al.: Automatic classification system for the diagnosis of alzheimer disease using Component-Based SVM aggregations. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) Advances in Neuro-Information Processing, pp. 402–409. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03040-6_49

    Chapter  Google Scholar 

  19. Khedher, L., Ramírez, J., Górriz, J.M., Brahim, A., Illán, I.A.: Independent component analysis-based classification of Alzheimer’s disease from segmented MRI data. In: Vicente, J.M.F., Álvarez-Sánchez, J.R., de la Paz, F., López, F.J.T.-M., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9107, pp. 78–87. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18914-7_9

    Chapter  Google Scholar 

  20. Sheng, J., et al.: A novel joint HCPMMP method for automatically classifying Alzheimer’s and different stage MCI patients. Behav. Brain Res. 365, 210–221 (2019)

    Article  Google Scholar 

  21. Mishra, S., Beheshti, I., Khanna, P., Initiative, A.D.N.: A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer’s disease. Int. J. Imaging Syst. Technol. 28(4), 302–314 (2018)

    Article  Google Scholar 

  22. Islam, J., Zhang, Y.: A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In: Zeng, Y., et al. (eds.) BI 2017. LNCS (LNAI), vol. 10654, pp. 213–222. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70772-3_20

    Chapter  Google Scholar 

  23. Jain, R., Jain, N., Aggarwal, A., Hemanth, D.J.: Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn. Syst. Res. 57, 147–159 (2019)

    Article  Google Scholar 

  24. Dimiduk, D.M., Holm, E.A., Niezgoda, S.R.: Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integr. Mater. Manuf. Innov. 7(3), 157–172 (2018)

    Article  Google Scholar 

  25. Tanveer, M., et al.: Machine learning techniques for the diagnosis of Alzheimer’s disease: a review. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 16(1s), 1–35 (2020)

    Google Scholar 

  26. 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 (2018). arXiv preprint arXiv:1801.05968

  27. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

    Article  Google Scholar 

  28. Glozman, T., Liba, O.: Hidden cues: Deep learning for Alzheimer’s disease classification CS331B project final report (2016)

    Google Scholar 

  29. Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.: Residual and plain convolutional neural networks for 3D brain MRI classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 835–838. IEEE (2017)

    Google Scholar 

  30. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (2019))

    Google Scholar 

  31. Isensee, F., Petersen, J., Kohl, S.A., Jäger, P.F., Maier-Hein, K.H.: nnu-net: Breaking the spell on successful medical image segmentation, vol. 1, pp. 1–8. arXiv preprint arXiv:1904.08128 (2019)

  32. Suthaharan, S.: Support vector machine. In: Machine learning models and algorithms for big data classification. ISIS, vol. 36, pp. 207–235. Springer, Boston, MA (2016). https://doi.org/10.1007/978-1-4899-7641-3_9

    Chapter  MATH  Google Scholar 

  33. Biau, G., Scornet, E.: A random forest guided tour. TEST 25(2), 197–227 (2016). https://doi.org/10.1007/s11749-016-0481-7

    Article  MathSciNet  MATH  Google Scholar 

  34. Webb, G.I., Keogh, E., Miikkulainen, R.: Naïve bayes. Encycl. Mach. Learn. 15, 713–714 (2010)

    Google Scholar 

  35. Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39964-3_62

    Chapter  Google Scholar 

  36. Buhrmester, V., Münch, D., Arens, M.: Analysis of explainers of black box deep neural networks for computer vision: a survey (2019). arXiv preprint arXiv:1911.12116

  37. Hon, M., Khan, N.M.: Towards Alzheimer's disease classification through transfer learning. In: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM), pp. 1166–1169. IEEE (2017)

    Google Scholar 

  38. Maqsood, M., et al.: Transfer learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI scans. Sensors 19(11), 2645 (2019)

    Article  Google Scholar 

  39. Oh, K., Chung, Y.C., Kim, K.W., Kim, W.S., Oh, I.S.: Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci. Rep. 9(1), 1–16 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Prof. Ram Meghe Institute of Research and Technology, Badnera, Amravati, 444701, India

    Archana Wamanrao Bhade & G. R. Bamnote

Authors
  1. Archana Wamanrao Bhade
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  2. G. R. Bamnote
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Correspondence to Archana Wamanrao Bhade .

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Editors and Affiliations

  1. Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura, India

    Apu Kumar Saha

  3. School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia

    Mukesh Prasad

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Bhade, A.W., Bamnote, G.R. (2023). Performance Evaluation of Machine and Deep Transfer Learning Techniques for the Classification of Alzheimer Disease Using MRI Images. In: Sharma, H., Saha, A.K., Prasad, M. (eds) Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022). ICIVC 2022. Proceedings in Adaptation, Learning and Optimization, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-031-31164-2_26

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