Enhancing Early Alzheimer's Disease Detection: Leveraging Pre-trained Networks and Transfer Learning

Full Text (PDF, 907KB), PP.52-69

Views: 0 Downloads: 0


Naveen. N. 1,* Nagaraj. G. Cholli 2

1. Department of Computer Science & Engineering, M. S. Ramaiah University of Applied Sciences, Visvesvaraya Technological University, Belagavi, 590018, India

2. Department of Information Science & Engineering, RV College of Engineering, Visvesvaraya Technological University, Belagavi, 590018, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2024.01.05

Received: 19 Sep. 2023 / Revised: 26 Oct. 2023 / Accepted: 7 Dec. 2023 / Published: 8 Feb. 2024

Index Terms

VGG, Inception, Residual Network (ResNet), Convolutional Neural Network (CNN), Transfer Learning (TL), Magnetic Resonance Imaging (MRI), Deep Neural Networks (DNN)


Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide. Early and accurate AD detection is crucial for timely intervention and improving patient outcomes. Lately, there have been notable advancements in using deep learning approaches to classify neuroimaging data associated with Alzheimer's disease. These methods have shown substantial progress in achieving accurate classification results. Nevertheless, the concept of end-to-end learning, which has the potential to harness the benefits of deep learning fully, has yet to garner extensive focus in the realm of neuroimaging. This is attributed mainly to the persistent challenge in neuroimaging, namely the limited data availability. This study employs neuroimages and Transfer Learning (TL) to identify early signs of AD and different phases of cognitive impairment. By employing transfer learning, the study uses Magnetic Resonance Imaging (MRI) images from the Alzheimer's Disease Neuroimaging (ADNI) database to classify images into various categories, such as Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). The classification task involves training and testing three pre-trained networks: VGG-19, ResNet-50, and Inception V3. The study evaluates the performance of these networks using the confusion matrix and its associated metrics. Among the three models, ResNet-50 achieves the highest recall rate of 99.25%, making it more efficient in detecting the early stages of AD development. The study further examines the performance of the pre-trained networks on a class-by-class basis using the parameters derived from the confusion matrix. This comprehensive analysis provides insights into how each model performs for different classes within the AD classification framework. Overall, the research underscores the potential of deep learning and transfer learning in advancing early AD detection and emphasizes the significance of utilizing pre-trained models for this purpose.

Cite This Paper

Naveen. N., Nagaraj. G. Cholli, "Enhancing Early Alzheimer's Disease Detection: Leveraging Pre-trained Networks and Transfer Learning", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.1, pp.52-69, 2024. DOI:10.5815/ijisa.2024.01.05


[1]S. Srivastava, R. Ahmad, and S. K. Khare, “Alzheimer’s disease and its treatment by different approaches: A review,” Eur. J. Med. Chem., vol. 216, Apr. 2021, doi: 10.1016/J.EJMECH.2021.113320.
[2]R. Brookmeyer, E. Johnson, K. Ziegler‐Graham, and H. M. Arrighi, “Forecasting the global burden of alzheimer’s disease,” Alzheimer’s & Dementia, vol. 3, no. 3, pp. 186–191, 2007. doi:10.1016/j.jalz.2007.04.381.
[3]G. Livingston, J. Huntley, A. Sommerlad, D. Ames, C. Ballard, S. Banerjee, et al., “Dementia prevention, intervention, and care: 2020 report of The Lancet Commission,” The Lancet, vol. 396, no. 10248, pp. 413–446, 2020. doi:10.1016/s0140-6736(20)30367-6.
[4]M. Dadar, A. L. Manera, S. Ducharme, and D. L. Collins, “White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer’s disease and frontotemporal dementia,” Neurobiol. Aging, vol. 111, pp. 54–63, Mar. 2022, doi: 10.1016/J.NEUROBIOLAGING.2021.11.007.
[5]K. Aderghal, A. Khvostikov, A. Krylov, J. Benois-Pineau, K. Afdel, and G. Catheline, “Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning,” Proc. - IEEE Symp. Comput. Med. Syst., vol. 2018-June, pp. 345–350, Jul. 2018, doi: 10.1109/CBMS.2018.00067.
[6]R. Petersen, “Early diagnosis of alzheimers disease: Is MCI too late?” Current Alzheimer Research, vol. 6, no. 4, pp. 324–330, 2009. doi:10.2174/156720509788929237.
[7]A. Tahami Monfared, M. J. Byrnes, L. A. White, and Q. Zhang, “Alzheimer’s disease: Epidemiology and clinical progression,” Neurology and Therapy, vol. 11, no. 2, pp. 553–569, 2022. doi:10.1007/s40120-022-00338-8.
[8]D.P. Veitch, M.W. Weiner, P.S. Aisen, L.A. Beckett, N.J. Cairns, R.C. Green, et al., “Understanding disease progression and improving Alzheimer’s disease clinical trials: Recent highlights from the Alzheimer’s Disease Neuroimaging Initiative,” Alzheimers. Dement., vol. 15, no. 1, pp. 106–152, Jan. 2019, doi: 10.1016/J.JALZ.2018.08.005.
[9]C. Plant, S.J. Teipel, A. Oswald, C. Böhm, T. Meindl, J. Mourao-Miranda, et al., “Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease,” Neuroimage, vol. 50, no. 1, pp. 162–174, Mar. 2010, doi: 10.1016/J.NEUROIMAGE.2009.11.046.
[10]S. S. Kundaram and K. C. Pathak, “Deep Learning-Based Alzheimer Disease Detection,” Lect. Notes Electr. Eng., vol. 673, pp. 587–597, 2021, Accessed: Apr. 05, 2023. [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-15-5546-6_50
[11]M. Tamoor and I. Younas, “Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model,” J. Xray. Sci. Technol., vol. 29, no. 4, pp. 721–739, 2021, doi: 10.3233/XST-210879.
[12]X. Liu, C. Wang, J. Bai, and G. Liao, “Fine-tuning Pre-trained Convolutional Neural Networks for Gastric Precancerous Disease Classification on Magnification Narrow-band Imaging Images,” Neurocomputing, vol. 392, pp. 253–267, Jun. 2020, doi: 10.1016/J.NEUCOM.2018.10.100.
[13]M. Hon and N. M. Khan, “Towards Alzheimer’s Disease Classification through Transfer Learning,” Proc. - 2017 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2017, vol. 2017-Janua, pp. 1166–1169, Nov. 2017, doi: 10.1109/BIBM.2017.8217822.
[14]S. Sarraf and G. Tofighi, “Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data,” FTC 2016 - Proc. Futur. Technol. Conf., pp. 816–820, Jan. 2017, doi: 10.1109/FTC.2016.7821697.
[15]J. Islam and Y. Zhang, “Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks,” Brain Informatics, vol. 5, no. 2, pp. 1–14, Dec. 2018, doi: 10.1186/S40708-018-0080-3/FIGURES/10.
[16]K. Oh, Y. C. Chung, K. W. Kim, W. S. Kim, and I. S. Oh, “Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning,” Sci. Reports 2019 91, vol. 9, no. 1, pp. 1–16, Dec. 2019, doi: 10.1038/s41598-019-54548-6.
[17]R. Jain, N. Jain, A. Aggarwal, and D. J. Hemanth, “Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images,” Cogn. Syst. Res., vol. 57, pp. 147–159, Oct. 2019, doi: 10.1016/J.COGSYS.2018.12.015.
[18]M. Liu, D. Zhang, and D. Shen, “Identifying informative imaging biomarkers via tree structured sparse learning for AD diagnosis,” Neuroinformatics, vol. 12, no. 3, pp. 381–394, Dec. 2014, doi: 10.1007/S12021-013-9218-X/METRICS.
[19]X. Zhao and X. M. Zhao, “Deep learning of brain magnetic resonance images: A brief review,” Methods, vol. 192, pp. 131–140, Aug. 2021, doi: 10.1016/J.YMETH.2020.09.007.
[20]Ebrahimi, S. Luo, and for the A. Disease Neuroimaging Initiative, “Convolutional neural networks for Alzheimer’s disease detection on MRI images,” J. Med. imaging (Bellingham, Wash.), vol. 8, no. 2, Apr. 2021, doi: 10.1117/1.JMI.8.2.024503.
[21]Khan and S. Zubair, “Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer’s disease,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 8000–8018, Nov. 2022, doi: 10.1016/J.JKSUCI.2022.07.016.
[22]B. Lei, E. Liang, M. Yang, P. Yang, F. Zhou, E.-L. Tan, et al., “Predicting clinical scores for Alzheimer’s disease based on joint and deep learning,” Expert Syst. Appl., vol. 187, p. 115966, Jan. 2022, doi: 10.1016/J.ESWA.2021.115966.
[23]Revathi, R. Kaladevi, K. Ramana, R. H. Jhaveri, M. Rudra Kumar, and M. Sankara Prasanna Kumar, “Early Detection of Cognitive Decline Using Machine Learning Algorithm and Cognitive Ability Test,” Secur. Commun. Networks, vol. 2022, 2022, doi: 10.1155/2022/4190023.
[24]N. Mahendran and D. R. V. P M, “A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer’s disease,” Comput. Biol. Med., vol. 141, Feb. 2022, doi: 10.1016/J.COMPBIOMED.2021.105056.
[25]T. M. Ghazal et al., “Alzheimer Disease Detection Empowered with Transfer Learning,” Comput. Mater. Contin., vol. 70, no. 3, pp. 5005–5019, Oct. 2021, doi: 10.32604/CMC.2022.020866.
[26]M. Odusami, R. Maskeliūnas, and R. Damaševičius, “An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging,” Sensors 2022, Vol. 22, Page 740, vol. 22, no. 3, p. 740, Jan. 2022, doi: 10.3390/S22030740.