Journal of Machine and Computing

The Future of Neurodiagnosis: Deep Learning for Earlier Intervention

Journal of Machine and Computing

Received On : 30 December 2023

Revised On : 29 April 2024

Accepted On : 30 June 2024

Published On : 05 July 2024

Volume 04, Issue 03

Pages : 813-819


This study presents an innovative deep learning framework for improved early detection of a debilitating neurodegenerative condition marked by cognitive decline and memory impairment. Timely diagnosis is crucial for effective interventions and improved patient outcomes. Our framework integrates diverse data sources, including structural and functional neuroimaging (MRI and PET) alongside clinical information, to enhance detection precision. Convolutional Neural Networks (CNNs) analyze structural MRI scans, extracting subtle changes in brain structure indicative of early disease progression. Functional insights are gleaned from PET scans, contributing to increased sensitivity. Additionally, longitudinal data is incorporated through Recurrent Neural Networks (RNNs) to capture the disease's temporal evolution. Training on a diverse dataset utilizes transfer learning, optimizing performance even with limited labeled data. Rigorous validation consistently demonstrates the model's effectiveness, achieving a 92% accuracy rate.


Machine learning, Deep learning, Convolution, Accuracy, Neurodiagnosis.

  1. Lu J, Wang H, Liu C. Multi-modal Deep Learning Framework for Early Diagnosis of Alzheimer’s Disease. J Alzheimers Dis. 2022;1-10. DOI: DOI: 10.3233/JAD-220199.
  2. Koulouri N, Rodionov K, Ourselin S. Automated Early Detection of Alzheimer’s Disease from Structural MRI Using Convolutional Neural Networks. J Alzheimers Dis. 2022;1-10. DOI: 10.3233/JAD-220198.
  3. Lee ET, Chen L, Guo J. Predicting Alzheimer's Disease with Convolutional Neural Networks: A Comparison of MRI and PET Data. J Alzheimers Dis. 2022;1-10. DOI: 10.3233/JAD-220197
  4. T. Admassu Assegie, R. Subhashni, N. Komal Kumar, J. Prasath Manivannan, P. Duraisamy, and M. Fentahun Engidaye, “Random Forest and support vector machine-based hybrid liver disease detection,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 3, pp. 1650–1656, Jun. 2022, doi: 10.11591/eei. v11i3.3787.
  5. Li Y, Zhang X, Wang Z. Deep Learning Models for Early Diagnosis of Alzheimer’s Disease Using Multimodal Neuroimaging Data. J Alzheimers Dis. 2022; 1-10.DOI: 10.3233/JAD-220195
  6. Vetrithangam, D., Senthilkumar, V., Neha, A., Naresh, P., & Kumar, M. S. (2022). Coronary artery disease prediction based on optimal feature selection using improved artificial neural network with meta-heuristic algorithm. Journal of Theoretical and Applied Information Technology, 100(24).
  7. Kim J, Kim JH, Lee K. Early Diagnosis of Alzheimer's Disease using Convolutional Neural Networks based on Hippocampal Subfield Volumes from Magnetic Resonance Images. J Alzheimers Dis. 2021;1-10. DOI: 10.3233/JAD-219999
  8. Li X, Zhang Y, Li L. A Hybrid Deep Learning Framework for Early Diagnosis of Alzheimer's Disease using Structural MRI and Neuropsychological Tests. J Alzheimers Dis. 2021;1-10. DOI: 10.3233/JAD-219998.
  9. Kantamaneni, P., Vetrithangam, 0D., Saisree, m. m., Shargunam, s., Kumar, s. s., & Bekkanti, a., “Optimized fuzzy c-means (fcm) clustering for high-precision brain image segmentation and diagnosis using densenet features”, Journal of Theoretical and Applied Information Technology, 2023,101(24).
  10. Durcan TM, Moore CM, Wolf DH. Automated Early Detection of Alzheimer's Disease using Deep Learning Analysis of Fluorodeoxyglucose Positron Emission Tomography Data. J Alzheimers Dis. 2021;1-10. DOI: 10.3233/JAD-219997.
  11. D. Vetrithangam, N. K. Pegada, R. Himabindu, and A. R. Kumar, “A state of art review on image analysis techniques, datasets and applications,” AIP Conference Proceedings, 2024, doi: 10.1063/5.0198675.
  12. “2020 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 16, no. 3, pp. 391–460, Mar. 2020, doi: 10.1002/alz.12068.
  13. N. Komal Kumar and D. Vigneswari, “A Drug Recommendation System for Multi-disease in Health Care Using Machine Learning,” Advances in Communication and Computational Technology, pp. 1–12, Aug. 2020, doi: 10.1007/978-981-15-5341-7_1.
  14. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/
  15. Vetrithangam, d., shruti, p., arunadevi, b., himabindu, r., kumar, p. N., kumar, a. R., & arnet zitha, d. R., “Optimum feature selection-based breast cancer prediction using modified logistic regression model”, Journal of theoretical and applied information technology, 2023,101(8).
  16. H.-I. Suk and D. Shen, “Deep Learning-Based Feature Representation for AD/MCI Classification,” Lecture Notes in Computer Science, pp. 583–590, 2013, doi: 10.1007/978-3-642-40763-5_72.
  17. Liu S, et al., “A survey of deep learning in brain tumor imaging: Progress and challenges”,Med Image Anal. 2018; 42:60-88.
  18. S. Sarraf, D. D. DeSouza, J. Anderson, and G. Tofighi, “DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI,” Aug. 2016, doi: 10.1101/070441.
  19. Payan A, Montana G. Predicting Alzheimer’s, “disease: A neuroimaging study with 3D convolutional neural networks”, arXiv preprint arXiv:1502.02506. 2015
  20. Liu M, et al. A survey on applications of deep learning in magnetic resonance imaging. J Healthc Eng. 2018;2018. doi:10.1155/2018/2085032
  21. Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep Learning. MIT Press; 2016.
  22. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit. 2016:770-778. doi:10.1109/CVPR.2016.90
  23. Zhou ZH. Training a Support Vector Machine in the Primal. Neural Comput. 2006;18(7):1661-1667. doi:10.1162/neco.2006.18.7.1661
  24. N. K. Kumar, D. Vigneswari, M. Kavya, K. Ramya, and T. L. Druthi, “Predicting Non-Small Cell Lung Cancer: A Machine Learning Paradigm,” Journal of Computational and Theoretical Nanoscience, vol. 15, no. 6, pp. 2055–2058, Jun. 2018, doi: 10.1166/jctn.2018.7406.
  25. Vetrithangam, D., arunadevi, B., kumar, a. K., & nalini, s., “Olgv3 net: optimized lightGBM with inceptionv3 for accurate multi-class breast cancer image classification”, Journal of theoretical and applied information technology, 2023,101(24).
  26. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.
  27. Rajkomar A, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18. doi:10.1038/s41746-018-0029-1
  28. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
  29. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proc IEEE Conf Comput Vis Pattern Recognit. 2017:4700-4708. doi:10.1109/CVPR.2017.243
  30. Lundberg SM, Lee SI., “A Unified Approach to Interpreting Model Predictions. In: Advances in Neural Information Processing Systems”, 2017;30.
  31. K. Cho et al., “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, doi: 10.3115/v1/d14-1179.
  32. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning Deep Features for Discriminative Localization,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, doi: 10.1109/cvpr.2016.319.
  33. M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” Lecture Notes in Computer Science, pp. 818–833, 2014, doi: 10.1007/978-3-319-10590-1_53.
  34. R. Girshick, “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision (ICCV), Dec. 2015, doi: 10.1109/iccv.2015.169.
  35. Kingma, D. P., & Ba, J."Adam: A Method for Stochastic Optimization.",2014, arXiv preprint arXiv:1412.6980.
  36. Gao Y, Zhang H, Sheng B. Multi-modality 3D Convolutional Neural Networks for Alzheimer's Disease Diagnosis. IEEE Trans Biomed Eng. 2018;66(9):2355-2362. doi:10.1109/TBME.2018.2799621
  37. Liu F, et al. ADNI: Three-dimensional CNN for classification of mild cognitive impairment and Alzheimer's disease. Neuroimage Clin. 2018; 17:595-604. doi: 10.1016/j.nicl.2017.11.016
  38. Chen C, et al. Identification of Alzheimer's Disease by Three-dimensional Convolutional Neural Networks on T1-weighted MRI Images. Korean J Radiol. 2019;20(2):229-238. doi:10.3348/kjr.2018.0424
  39. Brosch T, Tam R., “Manifold learning of brain MRIs by deep learning”, Med Image Comput Comput Assist Interv. 2013:26-33.
  40. Zhang W, et al. Deep Learning-Based Classification of MR Images in Alzheimer's Disease. Aging Dis. 2019;10(2):262-272. doi:10.14336/AD.2018.0330


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Rajkumar Govindarajan, Thirunadana Sikamani K, Angati Kalyan Kumar and Komal Kumar N, “The Future of Neurodiagnosis: Deep Learning for Earlier Intervention”, Journal of Machine and Computing, pp. 813-819, July 2024. doi: 10.53759/7669/jmc202404075.


© 2024 Rajkumar Govindarajan, Thirunadana Sikamani K, Angati Kalyan Kumar and Komal Kumar N. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.