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Alzheimer’s Disease Evaluation Through Visual Explainability by Means of Convolutional Neural Networks

    https://doi.org/10.1142/S0129065724500072Cited by:1 (Source: Crossref)
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    Abstract

    Background and Objective: Alzheimer’s disease is nowadays the most common cause of dementia. It is a degenerative neurological pathology affecting the brain, progressively leading the patient to a state of total dependence, thus creating a very complex and difficult situation for the family that has to assist him/her. Early diagnosis is a primary objective and constitutes the hope of being able to intervene in the development phase of the disease. Methods: In this paper, a method to automatically detect the presence of Alzheimer’s disease, by exploiting deep learning, is proposed. Five different convolutional neural networks are considered: ALEX_NET, VGG16, FAB_CONVNET, STANDARD_CNN and FCNN. The first two networks are state-of-the-art models, while the last three are designed by authors. We classify brain images into one of the following classes: non-demented, very mild demented and mild demented. Moreover, we highlight on the image the areas symptomatic of Alzheimer presence, thus providing a visual explanation behind the model diagnosis. Results: The experimental analysis, conducted on more than 6000 magnetic resonance images, demonstrated the effectiveness of the proposed neural networks in the comparison with the state-of-the-art models in Alzheimer’s disease diagnosis and localization. The best results in terms of metrics are the best with STANDARD_CNN and FCNN with accuracy, precision and recall between 98% and 95%. Excellent results also from a qualitative point of view are obtained with the Grad-CAM for localization and visual explainability. Conclusions: The analysis of the heatmaps produced by the Grad-CAM algorithm shows that in almost all cases the heatmaps highlight regions such as ventricles and cerebral cortex. Future work will focus on the realization of a network capable of analyzing the three anatomical views simultaneously.

    References

    • 1. M. Ahmadlou, A. Adeli, R. Bajo and H. Adeli, Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task, Clin. Neurophysiol. 125(4) (2014) 694–702. Crossref, Medline, Web of Science, Google Scholar
    • 2. J. L. Cummings and G. Cole, Alzheimer disease, JAMA 287(18) (2002) 2335–2338. Crossref, Medline, Web of Science, Google Scholar
    • 3. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease, Neurosci. Lett. 444(2) (2008) 190–194. Crossref, Medline, Web of Science, Google Scholar
    • 4. M. Ahmadlou, H. Adeli and A. Adeli, New diagnostic EEG markers of the Alzheimer’s disease using visibility graph, J. Neural Transm. 117 (2010) 1099–1109. Crossref, Medline, Web of Science, Google Scholar
    • 5. M. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease, Alzheimer Dis. Assoc. Disord. 25(1) (2011) 85–92. Crossref, Medline, Web of Science, Google Scholar
    • 6. Z. Sankari, H. Adeli and A. Adeli, Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease, Clin. Neurophysiol. 122(5) (2011) 897–906. Crossref, Medline, Web of Science, Google Scholar
    • 7. S. Hulbert and H. Adeli, EEG/MEG- and imaging-based diagnosis of Alzheimer’s disease, Rev. Neurosci. 24(6) (2013) 563–576. Crossref, Medline, Web of Science, Google Scholar
    • 8. E. Perez-Valero, C. Morillas, M. A. Lopez-Gordo and J. Minguillon, Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis, Int. J. Neural Syst. 33(4) (2023) 2350021. Link, Web of Science, Google Scholar
    • 9. C. Porcaro, F. Vecchio, F. Miraglia, G. Zito and P. M. Rossini, Dynamics of the “cognitive” brain wave P3b at rest for Alzheimer dementia prediction in mild cognitive impairment, Int. J. Neural Syst. 32(5) (2022) 2250022. Link, Web of Science, Google Scholar
    • 10. J. P. Amezquita-Sanchez, N. Mammone, F. C. Morabito and H. Adeli, A new dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms, Clin. Neurol. Neurosurg. 201 (2021) 106446. Crossref, Medline, Web of Science, Google Scholar
    • 11. N. Mammone, C. Ieracitano, H. Adeli, A. Bramanti and F. C. Morabito, Permutation Jaccard distance-based hierarchical clustering to estimate EEG network density modifications in MCI subjects, IEEE Trans. Neural Netw. Learn. Syst. 29(10) (2018) 5122–5135. Crossref, Web of Science, Google Scholar
    • 12. S. Bhat, U. R. Acharya, N. Dadmehr and H. Adeli, Clinical neurophysiological and automated EEG-based diagnosis of the Alzheimer’s disease, Eur. Neurol. 74(3–4) (2015) 202–210. Crossref, Medline, Web of Science, Google Scholar
    • 13. J. P. Amezquita-Sanchez, A. Adeli and H. Adeli, A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG), Behav. Brain Res. 305 (2016) 174–180. Crossref, Medline, Web of Science, Google Scholar
    • 14. R. Xian, R. Lugu, H. Peng, Q. Yang, X. Luo and J. Wang, Edge detection method based on nonlinear spiking neural systems, Int. J. Neural Syst. 33(1) (2023) 2250060. Link, Web of Science, Google Scholar
    • 15. F. Mercaldo, G. Ciaramella, G. Iadarola, M. Storto, F. Martinelli and A. Santone, Towards explainable quantum machine learning for mobile malware detection and classification, Appl. Sci. 12(23) (2022) 12025. Crossref, Google Scholar
    • 16. Z. Sankari and H. Adeli, Probabilistic neural networks for diagnosis of Alzheimer’s disease using conventional and wavelet coherence, J. Neurosci. Methods 197(1) (2011) 165–170. Crossref, Medline, Web of Science, Google Scholar
    • 17. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer’s disease and models of computation: Imaging, classification, and neural models, J. Alzheimer’s Dis. 7(3) (2005) 187–199. Crossref, Medline, Web of Science, Google Scholar
    • 18. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer’s disease: Models of computation and analysis of EEGs, Clin. EEG Neurosci. 36(3) (2005) 131–140. Crossref, Medline, Web of Science, Google Scholar
    • 19. O. K. Cura, A. Akan, G. C. Yilmaz and H. S. Ture, Detection of Alzheimer’s dementia by using signal decomposition and machine learning methods, Int. J. Neural Syst. 32(9) (2022) 2250042. Link, Web of Science, Google Scholar
    • 20. C. Jimenez-Mesa, J. E. Arco, M. Valentí-Soler, B. Frades-Payo, M. A. Zea-Sevilla, A. Ortiz, M. Ávila-Villanueva, D. Castillo-Barnes, J. Ramírez, T. Del Ser-Quijano, C. Carnero-Pardo and J. M. Górriz, Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern, Int. J. Neural Syst. 33(4) (2023) 2350015. Link, Web of Science, Google Scholar
    • 21. M. Di Giammarco, G. Iadarola, F. Martinelli, F. Mercaldo, F. Ravelli and A. Santone, Explainable deep learning for Alzheimer disease classification and localisation, in Int. Conf. Applied Intelligence and Informatics (Springer, 2022), pp. 129–143. Crossref, Google Scholar
    • 22. A. Farooq, S. Anwar, M. Awais and S. Rehman, A deep CNN based multi-class classification of Alzheimer’s disease using MRI, in 2017 IEEE Int. Conf. Imaging Systems and Techniques (IST) (IEEE, 2017), pp. 1–6. Google Scholar
    • 23. G. Folego, M. Weiler, R. F. Casseb, R. Pires and A. Rocha, Alzheimer’s disease detection through whole-brain 3D-CNN MRI, Front. Bioeng. Biotechnol. 8 (2020) 534592. Crossref, Medline, Web of Science, Google Scholar
    • 24. W. Lin, T. Tong, Q. Gao, D. Guo, X. Du, Y. Yang, G. Guo, M. Xiao, M. Du and X. Qu, Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment, Front. Neurosci. 12 (2018) 777. Crossref, Medline, Web of Science, Google Scholar
    • 25. A. W. Salehi, P. Baglat, B. B. Sharma, G. Gupta and A. Upadhya, A CNN model: Earlier diagnosis and classification of Alzheimer disease using MRI, in 2020 Int. Conf. Smart Electronics and Communication (ICOSEC) (IEEE, 2020), pp. 156–161. Crossref, Google Scholar
    • 26. J. Rieke, F. Eitel, M. Weygandt, J.-D. Haynes and K. Ritter, Visualizing convolutional networks for MRI-based diagnosis of Alzheimer’s disease, in Understanding and Interpreting Machine Learning in Medical Image Computing Applications (Springer, 2018), pp. 24–31. Crossref, Google Scholar
    • 27. Y. Fu’adah, I. Wijayanto, N. Pratiwi, F. Taliningsih, S. Rizal and M. Pramudito, Automated classification of Alzheimer’s disease based on MRI image processing using convolutional neural network (CNN) with AlexNet architecture, J. Phys., Conf. Ser. 1844(1) (2021) 012020. Crossref, Google Scholar
    • 28. A. Yiğit and Z. Işik, Applying deep learning models to structural MRI for stage prediction of Alzheimer’s disease, Turk. J. Electr. Eng. Comput. Sci. 28(1) (2020) 196–210. Crossref, Web of Science, Google Scholar
    • 29. S. Manimurugan, Classification of Alzheimer’s disease from MRI images using CNN based pre-trained VGG-19 model, J. Comput. Sci. Intell. Technol. 1(2) (2020) 15–21. Google Scholar
    • 30. N. J. Herzog and G. D. Magoulas, Convolutional neural networks-based framework for early identification of dementia using MRI of brain asymmetry, Int. J. Neural Syst. 32(12) (2022) 2250053. Link, Web of Science, Google Scholar
    • 31. G. Mirzaei and H. Adeli, Machine learning techniques for diagnosis of Alzheimer disease, mild cognitive disorder, and other types of dementia, Biomed. Signal Process. Control 72 (2022) 103293. Crossref, Web of Science, Google Scholar
    • 32. G. Mirzaei, A. Adeli and H. Adeli, Imaging and machine learning techniques for diagnosis of Alzheimer’s disease, Rev. Neurosci. 27(8) (2016) 857–870. Crossref, Medline, Web of Science, Google Scholar
    • 33. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh and D. Batra, Grad-CAM: Visual explanations from deep networks via gradient-based localization, in Proc. IEEE Int. Conf. Computer Vision (IEEE, 2017), pp. 618–626. Crossref, Google Scholar
    • 34. A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet classification with deep convolutional neural networks, in Proc. 25th Int. Conf. Advances in Neural Information Processing Systems (Curran Associates, 2012), pp. 1097–1105. Google Scholar
    • 35. K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, preprint (2014), arXiv:1409.1556. Google Scholar
    • 36. I. De Falco, G. De Pietro and G. Sannino, A two-step approach for classification in Alzheimer’s disease, Sensors 22(11) (2022) 3966. Crossref, Web of Science, Google Scholar
    • 37. Y. Liu, K. Tang, W. Cai, A. Chen, G. Zhou, L. Li and R. Liu, MPC-STANet: Alzheimer’s disease recognition method based on multiple phantom convolution and spatial transformation attention mechanism, Front. Aging Neurosci. 14 (2022) 918462. Crossref, Medline, Web of Science, Google Scholar
    • 38. S. Murugan, C. Venkatesan, M. Sumithra, X.-Z. Gao, B. Elakkiya, M. Akila and S. Manoharan, DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images, IEEE Access 9 (2021) 90319–90329. Crossref, Web of Science, Google Scholar
    • 39. G. B. Frisoni, N. C. Fox, C. R. Jack, P. Scheltens and P. M. Thompson, The clinical use of structural MRI in Alzheimer disease, Nat. Rev. Neurol. 6(2) (2010) 67–77. Crossref, Medline, Web of Science, Google Scholar
    • 40. S. Qiu et al., Multimodal deep learning for Alzheimer’s disease dementia assessment, Nat. Commun. 13(1) (2022) 3404. Crossref, Medline, Web of Science, Google Scholar
    • 41. H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel and X. Hu, Score-CAM: Scoreweighted visual explanations for convolutional neural networks, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (IEEE, 2020), pp. 24–25. Google Scholar
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