Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices

Zhen ZHAO, (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia)
Joon Huang CHUAH, (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia)
Chee-Onn CHOW, (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia)
Kaijian XIA, (Changshu Hospital Affiliated to Soochow University (Changshu No. 1 People’s Hospital), 1 Shuyuan St, Changshu, Suzhou, Jiangsu, PR China)
Yee Kai TEE, (Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malezya)
Yan Chai HUM, (Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malezya)
Khin Wee LAI (Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia)
Turkish Journal of Electrical Engineering and Computer Sciences
46 0
Yıl: 2024 Cilt: 32 Sayı: 1 Sayfa Aralığı: 93 - 107 Metin Dili: İngilizce DOI: 10.55730/1300-0632.4057 İndeks Tarihi: 14-03-2024

Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices

Öz:
Alzheimer’s disease (AD) is an illness that involves a gradual and irreversible degeneration of the brain. It is crucial to establish a precise diagnosis of AD early on in order to enable prompt therapies and prevent further deterioration. Researchers are currently focusing increasing attention on investigating the potential of machine learning techniques to simplify the automated diagnosis of AD using neuroimaging. The present study involved a comparison of models for the detection of AD through the utilization of 2D image slices obtained from magnetic resonance imaging brain scans. Five models, namely ResNet, ConvNeXt, CaiT, Swin Transformer, and CVT, were implemented to learn features and classify AD based on various perspectives of 2D image slices. A series of experiments were conducted using the dataset from the Alzheimer’s Disease Neuroimaging Initiative. The results showed that ConvNeXt outperformed ResNet, CaiT, Swin Transformer, and CVT. ConvNeXt exhibited an average accuracy, precision, recall, and F1 score of 95.74%, 96.71%, 95.74%, and 96.14%, respectively, when applied to a 3-way classification task involving AD, mild cognitive impairment, and normal control subjects. The results suggest that the utilization of ConvNeXt may have potential in the identification of AD using 2D slice images.
Anahtar Kelime: Alzheimer’s disease convolutional neural network transformer classification magnetic resonance imaging

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Nichols E, Steinmetz J, Vollset S, Fukutaki K, Chalek J et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease 2019. The Lancet Public Health 2022; 7 (2): e105-e125. https://doi.org/10.1016/S2468-2667(21)00249-8
  • [2] Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet 2020; 396 (10248): 413-446. https://doi.org/10.1016/S0140- 6736(20)30367-6
  • [3] Yeoh P, Lai K, Goh S, Hasikin K, Hum Y et al. Emergence of deep learning in knee osteoarthritis diagnosis. Com- putational Intelligence in Image and Video Analysis 2021; 2021: 4931437. https://doi.org/10.1155/2021/4931437
  • [4] Anis S, Lai K, Chuah J, Ali S, Mohafez H et al. An overview of deep learning approaches in chest radiograph. IEEE Access 2020; 8: 182347-182354. https://doi.org/10.1109/ACCESS.2020.3028390
  • [5] Jahanzad Z, Liew Y, Bilgen M, McLaughlin R, Leong C et al. Regional assessment of LV wall in infarcted heart using tagged MRI and cardiac modelling. Physics in Medicine & Biology 2015; 60 (10): 4015-4031. https://doi.org/10.1088/0031-9155/60/10/4015
  • [6] Chai H, Wee L, Swee T, Salleh S, Chea L. An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA). BioMedical Engineering OnLine 2011; 10: 87. https://doi.org/10.1186/1475-925X-10-87
  • [7] Zhao Z, Chuah J, Lai K, Chow C, Gochoo M et al. Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: a review. Frontiers in Computational Neuroscience 2023; 17. https://doi.org/10.3389/fncom.2023.1038636
  • [8] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556 2014.
  • [9] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR); Los Alamitos, CA, USA; 2016. pp. 770-778. https://doi.org/10.1109/CVPR.2016.90
  • [10] Huang G, Liu Z, Maaten L, Weinberger K. Densely connected convolutional networks. In: 2017 IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR); Los Alamitos, CA, USA; 2017. pp. 2261-2269. https://doi.org/10.1109/CVPR.2017.243
  • [11] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S. Going deeper with convolutions. In: 2015 IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR); Los Alamitos, CA, USA; 2015. pp. 1-9. https://doi.org/10.1109/CVPR.2015.7298594
  • [12] Valliani A, Soni A. Deep residual nets for improved Alzheimer’s diagnosis. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics; New York, NY, USA; 2017. p. 615. https://doi.org/10.1145/3107411.3108224
  • [13] Deng J, Dong W, Socher R, Li L, Li K et al. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops); Los Alamitos, CA, USA; 2009. pp. 248-255. https://doi.org/10.1109/CVPR.2009.5206848
  • [14] Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-Gonzalez J, Routier A et al. Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Medical Image Analysis 2020; 63: 101694. https://doi.org/10.1016/j.media.2020.101694
  • [15] Ellis K, Bush A, Darby D, De Fazio D, Foster J et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. International Psychogeriatrics 2009; 21 (4): 672-687. https://doi.org/10.1017/S1041610209009405
  • [16] Marcus D, Wang T, Parker J, Csernansky J, Morris J et al. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal Of Cognitive Neuroscience 2007; 19 (9): 1498-1507. https://doi.org/10.1162/jocn.2007.19.9.1498
  • [17] Puente-Castro A, Fernandez-Blanco E, Pazos A, Munteanu C. Automatic assessment of Alzheimer’s dis- ease diagnosis based on deep learning techniques. Computers in Biology and Medicine 2020; 120: 103764. https://doi.org/10.1016/j.compbiomed.2020.103764
  • [18] Lim B, Lai K, Haiskin K, Kulathilake K, Ong Z et al. Deep learning model for prediction of progressive mild cognitive impairment to Alzheimer’s disease using structural MRI. Frontiers in Aging Neuroscience 2022; 14: 876202. https://doi.org/10.3389/fnagi.2022.876202
  • [19] Bedel H, Sivgin I, Dalmaz O, Dar S, Cukur T. BolT: fused window transformers for fMRI time series analysis. Medical Image Analysis 2023; 88: 102841. https://doi.org/10.1016/j.media.2023.102841
  • [20] Sarraf S, Sarraf A, DeSouza D, Anderson J, Kabia M et al. OViTAD: optimized vision transformer to predict various stages of Alzheimer’s disease using resting-state fMRI and structural MRI data. Brain Sciences 2023; 13 (2): 260. https://doi.org/10.3390/brainsci13020260
  • [21] Ge C, Qu Q, Gu I, Jakola A. Multiscale deep convolutional networks for characterization and detection of Alzheimer’s disease using MR images. In: 2019 IEEE International Conference on Image Processing (ICIP); Taipei, Taiwan; 2019. pp. 789-793. https://doi.org/10.1109/ICIP.2019.8803731
  • [22] Qiu S, Miller M, Joshi P, Lee J, Xue C et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nature Communications 2022; 13: 3404. https://doi.org/10.1038/s41467-022-31037-5
  • [23] Jenkinson M, Beckmann C, Behrens T, Woolrich M, Smith S. FSL. NeuroImage 2012; 62 (2): 782-790. https://doi.org/10.1016/J.NEUROIMAGE.2011.09.015
  • [24] Smith S. Fast robust automated brain extraction. Human Brain Mapping 2002; 17 (3): 143-155. https://doi.org/10.1002/hbm.10062
  • [25] Tustison N, Avants B, Cook P, Zheng Y, Egan A et al. N4ITK: improved N3 bias correction. IEEE Transactions on Medical Imaging 2010; 29 (6): 1310-1320. https://doi.org/10.1109/TMI.2010.2046908
  • [26] Liu Z, Mao H, Wu C, Feichtenhofer C, Darrell T et al. A ConvNet for the 2020s. In: 2022 IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR); New Orleans, LA, USA; 2022. pp. 11966-11976. https://doi.org/10.1109/CVPR52688.2022.01167
  • [27] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L et al. Attention is all you need. Advances In Neural Information Processing Systems 2017; 30.
  • [28] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X et al. An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference On Learning Representations 2021.
  • [29] Liu Z, Lin Y, Cao Y, Hu H, Wei Y et al. Swin Transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); Montreal, QC, Canada; 2021. pp. 9992-10002. https://doi.org/10.1109/ICCV48922.2021.00986
  • [30] Touvron H, Cord M, Sablayrolles A, Synnaeve G, Jegou H. Going deeper with image transformers. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); Montreal, QC, Canada; 2021. pp. 32-42. https://doi.org/10.1109/ICCV48922.2021.00010
  • [31] Wu H, Xiao B, Codella N, Liu M, Dai X et al. CvT: introducing convolutions to vision transformers. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); Montreal, QC, Canada; 2021. pp. 22-31. https://doi.org/10.1109/ICCV48922.2021.00009
  • [32] Zhu H, Chen B, Yang C. Understanding why ViT trains badly on small datasets: an intuitive perspective. ArXiv Preprint ArXiv:2302.03751 2023.
  • [33] Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 2011; 56 (2): 766-781. https://doi.org/10.1016/j.neuroimage.2010.06.013
  • [34] Bedel H, Cukur T. DreaMR: diffusion-driven counterfactual explanation for functional MRI. ArXiv Preprint ArXiv:2307.09547 2023.
APA Zhao Z, Chuah J, Chow C, Xia K, Tee Y, hum y, Khin Wee L (2024). Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. , 93 - 107. 10.55730/1300-0632.4057
Chicago Zhao Zhen,Chuah Joon Huang,Chow Chee-Onn,Xia Kaijian,Tee Yee Kai,hum yan chai,Khin Wee Lai Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. (2024): 93 - 107. 10.55730/1300-0632.4057
MLA Zhao Zhen,Chuah Joon Huang,Chow Chee-Onn,Xia Kaijian,Tee Yee Kai,hum yan chai,Khin Wee Lai Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. , 2024, ss.93 - 107. 10.55730/1300-0632.4057
AMA Zhao Z,Chuah J,Chow C,Xia K,Tee Y,hum y,Khin Wee L Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. . 2024; 93 - 107. 10.55730/1300-0632.4057
Vancouver Zhao Z,Chuah J,Chow C,Xia K,Tee Y,hum y,Khin Wee L Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. . 2024; 93 - 107. 10.55730/1300-0632.4057
IEEE Zhao Z,Chuah J,Chow C,Xia K,Tee Y,hum y,Khin Wee L "Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices." , ss.93 - 107, 2024. 10.55730/1300-0632.4057
ISNAD Zhao, Zhen vd. "Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices". (2024), 93-107. https://doi.org/10.55730/1300-0632.4057
APA Zhao Z, Chuah J, Chow C, Xia K, Tee Y, hum y, Khin Wee L (2024). Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. Turkish Journal of Electrical Engineering and Computer Sciences, 32(1), 93 - 107. 10.55730/1300-0632.4057
Chicago Zhao Zhen,Chuah Joon Huang,Chow Chee-Onn,Xia Kaijian,Tee Yee Kai,hum yan chai,Khin Wee Lai Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. Turkish Journal of Electrical Engineering and Computer Sciences 32, no.1 (2024): 93 - 107. 10.55730/1300-0632.4057
MLA Zhao Zhen,Chuah Joon Huang,Chow Chee-Onn,Xia Kaijian,Tee Yee Kai,hum yan chai,Khin Wee Lai Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. Turkish Journal of Electrical Engineering and Computer Sciences, vol.32, no.1, 2024, ss.93 - 107. 10.55730/1300-0632.4057
AMA Zhao Z,Chuah J,Chow C,Xia K,Tee Y,hum y,Khin Wee L Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. Turkish Journal of Electrical Engineering and Computer Sciences. 2024; 32(1): 93 - 107. 10.55730/1300-0632.4057
Vancouver Zhao Z,Chuah J,Chow C,Xia K,Tee Y,hum y,Khin Wee L Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices. Turkish Journal of Electrical Engineering and Computer Sciences. 2024; 32(1): 93 - 107. 10.55730/1300-0632.4057
IEEE Zhao Z,Chuah J,Chow C,Xia K,Tee Y,hum y,Khin Wee L "Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices." Turkish Journal of Electrical Engineering and Computer Sciences, 32, ss.93 - 107, 2024. 10.55730/1300-0632.4057
ISNAD Zhao, Zhen vd. "Machine learning approaches in comparative studies for Alzheimer’s diagnosis using 2D MRI slices". Turkish Journal of Electrical Engineering and Computer Sciences 32/1 (2024), 93-107. https://doi.org/10.55730/1300-0632.4057
Sayfa Oluşturulma Tarihi
24-04-2024 10:55

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları