TY  - JOUR
T1  - Early diagnosis of Alzheimer’s Disease using hybrid CNN-Transformer models with Grad-CAM interpretability
TT  - Grad-CAM yorumlanabilirliği ile hibrit CNN-Transformer modeller kullanılarak Alzheimer Hastalığının erken tanısı
AU  - Kabakuş, Abdullah Talha
AU  - Erdoğmuş, Pakize
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.17714/gumusfenbil.1714884
JF  - Gümüşhane Üniversitesi Fen Bilimleri Dergisi
PB  - Gümüşhane Üniversitesi
WT  - DergiPark
SN  - 2146-538X
SP  - 829
EP  - 853
VL  - 15
IS  - 3
LA  - en
AB  - Detecting Alzheimer’s Disease (AD) at an early stage is vital because it enables prompt treatment and intervention, which can help slow disease progression and enhance patient prognosis. Given the increasing prevalence of AD globally, with an estimated 50 million people currently living with the condition and projected to triple by 2050, the development of accurate and efficient diagnostic tools is paramount. In this study, a novel architecture for the early diagnosis of AD by combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) with traditional Machine Learning (ML) algorithms was proposed. Utilizing MRI images as input, CNNs/ViTs serve as feature extractors, while demographic data is integrated to enhance diagnostic accuracy. Through extensive experimentation, our proposed model, which utilizes a CNN backbone optimized for MRI analysis as a feature extractor and LGBM as the classifier, achieved superior accuracy, reaching up to 96.83%. Statistical validation through confidence intervals and McNemar’s test further demonstrated the robustness and significant performance improvements of the proposed model compared to baseline methods. This study employs eXplainable AI techniques to visualize critical regions in MRI images that influence the model’s diagnostic decisions, promoting clinical transparency and trust in AI-assisted early diagnosis of AD. The novelty of this study lies in integrating deep feature extractors (CNNs/ViTs) with traditional ML classifiers, supported by interpretability through Grad-CAM and statistical validation, offering a transparent and accurate framework for early diagnosis of AD.
KW  - Alzheimer’s disease
KW  - Convolutional neural network
KW  - Dementia
KW  - Explainable AI
KW  - Magnetic resonance imaging
KW  - Vision transformer
N2  - Alzheimer Hastalığını (AH) erken evrede tespit etmek hızlı tedavi ve müdahaleye olanak sağlaması açısından çok önemlidir. Bu sayede hastalığın ilerlemesi yavaşlatılabilir ve hasta prognozu iyileştirilebilir. Dünya genelinde AH’nin artan yaygınlığı göz önüne alındığında — şu anda yaklaşık 50 milyon kişinin bu hastalıkla yaşadığı ve bu sayının 2050 yılına kadar üç katına çıkacağı öngörüldüğünde — doğru ve etkili tanı araçlarının geliştirilmesi kritik hale gelmiştir. Bu çalışmada, Konvolüsyonel Sinir Ağları (CNN’ler) veya Görüntü Dönüştürücüler (ViT’ler) ile geleneksel Makine Öğrenmesi (ML) algoritmalarını birleştirerek Alzheimer hastalığının erken tanısına yönelik özgün bir mimari sunulmaktadır. Girdi olarak MRI (Manyetik Rezonans Görüntüleme) görüntülerini kullanan CNN/ViT modelleri özellik çıkarıcı olarak işlev görmekte ve tanı doğruluğunu artırmak amacıyla demografik verilerle birleştirilmektedir. Gerçekleştirilen kapsamlı deneyler sonucunda, MRI analizi için optimize edilmiş bir CNN tabanlı özellik çıkarıcı ile LGBM sınıflandırıcısının kullanıldığı önerilen modelimiz %96,83’e varan doğruluk oranı ile üstün performans sergilemiştir. Güven aralıkları ve McNemar testi yoluyla yapılan istatistiksel doğrulamalar, önerilen modelin temel yöntemlere kıyasla sağlamlığını ve anlamlı performans iyileştirmelerini desteklemiştir. Bu çalışma, Açıklanabilir Yapay Zeka tekniklerini kullanarak modelin tanısal kararlarını etkileyen MRG görüntülerindeki kritik bölgeler görselleştirilmiş ve böylece yapay zeka destekli erken teşhis süreçlerinde klinik şeffaflık ve güven teşvik edilmiştir. Bu çalışmanın özgünlüğü, derin özellik çıkarıcıların (CNN’ler/ViT’ler) geleneksel ML sınıflandırıcılarıyla bütünleştirilmesinde yatmaktadır. Bu yapı, Grad-CAM tabanlı yorumlanabilirlik ve istatistiksel doğrulama ile desteklenerek, erken AH tanısı için şeffaf ve yüksek doğrulukta bir çerçeve sunmaktadır.
CR  - Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), 265–283.
CR  - Abrol, A., Bhattarai, M., Fedorov, A., Du, Y., Plis, S., & Calhoun, V. (2020). Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease. Journal of Neuroscience Methods, 339, 1–16. https://doi.org/10.1016/j.jneumeth.2020.108701
CR  - Agbavor, F., & Liang, H. (2022). Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer’s Disease Using Voice. Brain Sciences, 13(1), 1–13. https://doi.org/10.3390/brainsci13010028
CR  - Akalin, F. (2024). Survival Classification in Heart Failure Patients by Neural Network-Based Crocodile and Egyptian Plover (CEP) Optimization Algorithm. Arabian Journal for Science and Engineering, 49(3), 3897–3914. https://doi.org/10.1007/s13369-023-08183-z
CR  - Arafa, D. A., Moustafa, H. E. D., Ali-Eldin, A. M. T., & Ali, H. A. (2022). Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey. Multimedia Tools and Applications, 81(17), 23735–23776. https://doi.org/10.1007/s11042-022-11925-0
CR  - Arjaria, S. K., Rathore, A. S., Bisen, D., & Bhattacharyya, S. (2024). Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease. Annals of Data Science, 11, 307–335. https://doi.org/10.1007/s40745-022-00452-2
CR  - Armstrong, R. A. (2009). The molecular biology of senile plaques and neurofibrillary tangles in Alzheimer’s disease. Folia Neuropathologica, 47(4), 288–299.
CR  - Asl, E. H., Ghazal, M., Mahmoud, A., Aslantas, A., Shalaby, A., Casanova, M., Barnes, G., Gimel’farb, G., Keynton, R., & Baz, A. El. (2018). Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Frontiers in Bioscience - Landmark, 23(3), 584–596. https://doi.org/10.2741/4606
CR  - Bagade, V., & Godse, S. P. (2024). Early Detection of Alzheimer’s Disease based on the State-Of-The-Art Deep Learning Approach. Proceedings of 2024 IEEE Pune Section International Conference, PuneCon 2024, 1–7. https://doi.org/10.1109/PUNECON63413.2024.10895066
CR  - Balasundaram, A., Srinivasan, S., Prasad, A., Malik, J., & Kumar, A. (2023). Hippocampus Segmentation-Based Alzheimer’s Disease Diagnosis and Classification of MRI Images. Arabian Journal for Science and Engineering, 48, 10249–10265. https://doi.org/10.1007/s13369-022-07538-2
CR  - Bao, H., Dong, L., Piao, S., & Wei, F. (2022). BEiT: BERT Pre-Training of Image Transformers. Proceedings of the 10th International Conference on Learning Representations (ICLR 2022).
CR  - Basheer, S., Bhatia, S., & Sakri, S. B. (2021). Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset. IEEE Access, 9, 1–14. https://doi.org/10.1109/ACCESS.2021.3066213
CR  - Battineni, G., Chintalapudi, N., & Amenta, F. (2019). Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Informatics in Medicine Unlocked, 16, 1–8. https://doi.org/10.1016/j.imu.2019.100200
CR  - Chen, Q., Fu, Q., Bai, H., & Hong, Y. (2024). Longformer: Longitudinal Transformer for Alzheimer’s Disease Classification With Structural MRIs. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3575–3584.
CR  - Chollet, F. (2017). Deep Learning with Python. Manning Publications.
CR  - Chollet, F. (2024). Keras: the Python deep learning API. https://keras.io
CR  - Chui, K. T., Gupta, B. B., Alhalabi, W., & Alzahrani, F. S. (2022). An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning. Diagnostics, 12(7), 1–14. https://doi.org/10.3390/diagnostics12071531
CR  - Cilia, N. D., D’Alessandro, T., De Stefano, C., & Fontanella, F. (2022). Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer’s disease prediction. Machine Vision and Applications, 33, 1–17. https://doi.org/10.1007/s00138-022-01297-8
CR  - Cui, R., & Liu, M. (2019). RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Computerized Medical Imaging and Graphics, 73, 1–10. https://doi.org/10.1016/j.compmedimag.2019.01.005
CR  - Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li, & Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. Proceeding of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), 248–255. https://doi.org/10.1109/cvpr.2009.5206848
CR  - Diwate, R. B., Ghosh, R., Jha, R., Sagar, I., & Kumar Singh, S. (2021). Dementia Prediction Using OASIS Data for Alzheimer’s Research. Proceedings of the 2021 1st IEEE International Conference on Artificial Intelligence and Machine Vision (AIMV 2021), 1–7. https://doi.org/10.1109/AIMV53313.2021.9670900
CR  - Donders, A. R. T., van der Heijden, G. J. M. G., Stijnen, T., & Moons, K. G. M. (2006). Review: A gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59(10), 1087–1091. https://doi.org/10.1016/j.jclinepi.2006.01.014
CR  - Erdogmus, P., & Kabakus, A. T. (2023). The promise of convolutional neural networks for the early diagnosis of the Alzheimer’s disease. Engineering Applications of Artificial Intelligence, 123, 1–13. https://doi.org/10.1016/j.engappai.2023.106254
CR  - Fathi, S., Ahmadi, M., & Dehnad, A. (2022). Early diagnosis of Alzheimer’s disease based on deep learning: A systematic review. Computers in Biology and Medicine, 146, 1–16. https://doi.org/10.1016/j.compbiomed.2022.105634
CR  - Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. https://doi.org/10.1016/0022-3956(75)90026-6
CR  - Gasmi, K., Alyami, A., Hamid, O., Altaieb, M. O., Shahin, O. R., Ben Ammar, L., Chouaib, H., & Shehab, A. (2024). Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer’s Disease Using Adaptive Weight Selection. Diagnostics, 14(24), 2779. https://doi.org/10.3390/DIAGNOSTICS14242779
CR  - Google. (2023). google/vit-base-patch16-224. https://huggingface.co/google/vit-base-patch16-224
CR  - Grossberg, G. T., Tong, G., Burke, A. D., & Tariot, P. N. (2019). Present Algorithms and Future Treatments for Alzheimer’s Disease. Journal of Alzheimer’s Disease, 67(4), 1157–1171. https://doi.org/10.3233/JAD-180903
CR  - Haulcy, R., & Glass, J. (2021). Classifying Alzheimer’s Disease Using Audio and Text-Based Representations of Speech. Frontiers in Psychology, 11, 1–13. https://doi.org/10.3389/fpsyg.2020.624137
CR  - He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
CR  - Henschel, L., Kügler, D., & Reuter, M. (2022). FastSurferVINN: Building resolution-independence into deep learning segmentation methods—A solution for HighRes brain MRI. NeuroImage, 251, 1–22. https://doi.org/10.1016/j.neuroimage.2022.118933
CR  - Hollingshead, A. (1975). Four factor index of social status. In Yale Journal of Sociology (Vol. 8).
CR  - Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017-January. https://doi.org/10.1109/CVPR.2017.243
CR  - Huber, P. J. (1981). Robust Statistics. Wiley. https://doi.org/10.1002/0471725250
CR  - Hugging Face – The AI community building the future. (2024). Hugging Face. https://huggingface.co
CR  - Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science and Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
CR  - Ji, H., Liu, Z., Yan, W. Q., & Klette, R. (2019a). Early Diagnosis of Alzheimer’s Disease Based on Selective Kernel Network with Spatial Attention. Proceedings of the Asian Conference on Pattern Recognition 2019 (ACPR 2019), 12047 LNCS, 503–515. https://doi.org/10.1007/978-3-030-41299-9_39
CR  - Ji, H., Liu, Z., Yan, W. Q., & Klette, R. (2019b). Early diagnosis of Alzheimer’s disease using deep learning. Proceedings of the 2nd International Conference on Control and Computer Vision (ICCCV ’19), 87–91. https://doi.org/10.1145/3341016.3341024
CR  - Kaeberlein, M. (2013). Longevity and aging. F1000Prime Reports, 5(5), 1–8. https://doi.org/10.12703/P5-5
CR  - Kamada, S., Ichimura, T., & Harada, T. (2021). Image-Based Early Detection of Alzheimer’s Disease by Using Adaptive Structural Deep Learning. Proceedings of the Smart Innovation, Systems and Technologies 2021 (ICOMTA 2021), 238, 595–605. https://doi.org/10.1007/978-981-16-2765-1_49
CR  - Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022). Deep learning for Alzheimer’s disease diagnosis: A survey. Artificial Intelligence in Medicine, 130, 1–33. https://doi.org/10.1016/j.artmed.2022.102332
CR  - Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS’12), 1097–1105.
CR  - Lauraitis, A., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2020). A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment. Sensors (Switzerland), 20(11), 1–22. https://doi.org/10.3390/s20113236
CR  - Lazli, L. (2025). Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development. JMIRx Med, 6, e60866. https://doi.org/10.2196/60866
CR  - Leong, L. K., & Abdullah, A. A. (2019). Prediction of Alzheimer’s disease (AD) Using Machine Learning Techniques with Boruta Algorithm as Feature Selection Method. Journal of Physics: Conference Series, 1372, 1–8. https://doi.org/10.1088/1742-6596/1372/1/012065
CR  - Li, F., & Liu, M. (2018). Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Computerized Medical Imaging and Graphics, 70, 101–110. https://doi.org/10.1016/j.compmedimag.2018.09.009
CR  - Lin, C. J., & Lin, C. W. (2021). Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis. Sensors and Materials, 33(10), 3399–3413. https://doi.org/10.18494/SAM.2021.3512
CR  - Liu, M., Cheng, D., & Yan, W. (2018). Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. Frontiers in Neuroinformatics, 12, 1–12. https://doi.org/10.3389/fninf.2018.00035
CR  - Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. (2014). Early diagnosis of Alzheimer’s disease with deep learning. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014), 1015–1018. https://doi.org/10.1109/isbi.2014.6868045
CR  - Livni, R., Shalev-Shwartz, S., & Shamir, O. (2013). An Algorithm for Training Polynomial Networks. ArXiV, 1304.7045, 1–22.
CR  - Lukiw, W. J. (2012). Amyloid beta (Aβ) peptide modulators and other current treatment strategies for Alzheimer’s disease (AD). Expert Opinion on Emerging Drugs, 17(1), 1–27. https://doi.org/10.1517/14728214.2012.672559
CR  - Mahmud, T., Barua, K., Habiba, S. U., Sharmen, N., Hossain, M. S., & Andersson, K. (2024). An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning. Diagnostics, 14(3), 1–24. https://doi.org/10.3390/diagnostics14030345
CR  - Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2010). Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults. Journal of Cognitive Neuroscience, 22(12), 2677–2684. https://doi.org/10.1162/jocn.2009.21407
CR  - Matplotlib: Visualization with Python. (2024). https://matplotlib.org
CR  - McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157. https://doi.org/10.1007/BF02295996
CR  - Mehmood, A., yang, S., feng, Z., wang, M., Ahmad, A. S., khan, R., Maqsood, M., & Yaqub, M. (2021). A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images. Neuroscience, 460, 43–52. https://doi.org/10.1016/j.neuroscience.2021.01.002
CR  - Mienye, I. D., Swart, T. G., Obaido, G., Jordan, M., & Ilono, P. (2025). Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information, 16(3), 195. https://doi.org/10.3390/INFO16030195
CR  - Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414. https://doi.org/10.1212/wnl.43.11.2412-a
CR  - Neuroimaging in Python — NiBabel. (2024). https://nipy.org/nibabel
CR  - Ntampakis, N., Diamantaras, K., Argyriou, V., & Sarigianndis, P. (2024). Enhanced Deep Learning Methodologies and MRI Selection Techniques for Dementia Diagnosis in the Elderly Population. ArXiv, 2407.17324v2, 1–12.
CR  - Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191
CR  - Papanastasiou, G., Dikaios, N., Huang, J., Wang, C., & Yang, G. (2024). Is Attention all You Need in Medical Image Analysis? A Review. IEEE Journal of Biomedical and Health Informatics, 28(3), 1398–1411. https://doi.org/10.1109/JBHI.2023.3348436
CR  - Pappas, B. A., Bayley, P. J., Bui, B. K., Hansen, L. A., & Thal, L. J. (2000). Choline acetyltransferase activity and cognitive domain scores of Alzheimer’s patients. Neurobiology of Aging, 21(1), 11–17. https://doi.org/10.1016/S0197-4580(00)00090-7
CR  - Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
CR  - Qiu, S., Chang, G. H., Panagia, M., Gopal, D. M., Au, R., & Kolachalama, V. B. (2018). Fusion of deep learning models of MRI scans, Mini–Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 10, 737–749. https://doi.org/10.1016/j.dadm.2018.08.013
CR  - Raza, M. L., Hassan, S. T., Jamil, S., Hyder, N., Batool, K., Walji, S., & Abbas, M. K. (2025). Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions. Frontiers in Neuroinformatics, 19, 1557177. https://doi.org/10.3389/FNINF.2025.1557177/XML
CR  - Rehman Butt, A. U., Hamid, I., Nawaz, Q., Mahmood, T., Zhang, X., & Yaqub, M. (2024). A Novel Multi-Scale Deep Learning Approach for the Early Detection of Alzheimer’s Disease Using fMRI. Proceedings of 2024 5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024, 85–90. https://doi.org/10.1109/ICCBD-AI65562.2024.00022
CR  - Rhman, M., Rahman, F., Hossain, M. M., Emu, U. H., Akter, K., & Mridha, M. F. (2021). Predicting Alzheimer’s Disease at Low Cost Using Machine Learning. Proceedings of the 2021 International Conference on Science and Contemporary Technologies (ICSCT 2021), 1–5. https://doi.org/10.1109/ICSCT53883.2021.9642536
CR  - Selkoe, D. J. (2001). Alzheimer’s disease: Genes, proteins, and therapy. Physiological Reviews, 81(2), 741–766. https://doi.org/10.1152/physrev.2001.81.2.741
CR  - Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2017-October. https://doi.org/10.1109/ICCV.2017.74
CR  - Shanmugam, J. V., Duraisamy, B., Simon, B. C., & Bhaskaran, P. (2022). Alzheimer’s disease classification using pre-trained deep networks. Biomedical Signal Processing and Control, 71, 1–8. https://doi.org/10.1016/j.bspc.2021.103217
CR  - Shi, J., Zheng, X., Li, Y., Zhang, Q., & Ying, S. (2018). Multimodal Neuroimaging Feature Learning with Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer’s Disease. IEEE Journal of Biomedical and Health Informatics, 22(1), 173–183. https://doi.org/10.1109/JBHI.2017.2655720
CR  - Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298. https://doi.org/10.1109/TMI.2016.2528162
CR  - Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. In Y. Bengio & Y. LeCun (Eds.), Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015) (pp. 1–14).
CR  - Suk, H. Il, Lee, S. W., & Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582. https://doi.org/10.1016/j.neuroimage.2014.06.077
CR  - Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. https://doi.org/10.1109/CVPR.2015.7298594
CR  - Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020). Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis, 63, 101693. https://doi.org/10.1016/J.MEDIA.2020.101693
CR  - Tiriki, N. (2010). OASIS-2 Longitudinal Scan Data. Kaggle. https://www.kaggle.com/datasets/nadiatriki/oasis-2-longitudinal-scan-data
CR  - Vernekar, S. R., & Selva Kumar, S. (2024). Exploration of Explainable AI with Deep Learning Model for Early Detection of Alzheimer’s Disease. Proceedings of 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024, 1–6. https://doi.org/10.1109/CSITSS64042.2024.10816763
CR  - Waldo-Benítez, G., Padierna, L. C., Ceron, P., & Sosa, M. A. (2024). Dementia classification from magnetic resonance images by machine learning. Neural Computing and Applications, 36, 2653–2664. https://doi.org/10.1007/s00521-023-09163-y
CR  - Waskom, M. L. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 1–4. https://doi.org/10.21105/joss.03021
CR  - Wolf, T., Debut, L., Sanh, V., Chaumond, J., & ... (2020). Transformers: State-of-the-art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 1910.03771, 38–45.
CR  - Wong, T. H., Seelaar, H., Melhem, S., Rozemuller, A. J. M., & van Swieten, J. C. (2020). Genetic screening in early-onset Alzheimer’s disease identified three novel presenilin mutations. Neurobiology of Aging, 86, 201.e1-201.e14. https://doi.org/10.1016/j.neurobiolaging.2019.01.015
CR  - Ye, J., Chen, K., Wu, T., Li, J., Zhao, Z., Patel, R., Bae, M., Janardan, R., Liu, H., Alexander, G., & Reiman, E. (2008). Heterogeneous data fusion for alzheimer’s disease study. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’08), 1025–1033. https://doi.org/10.1145/1401890.1402012
CR  - Yildiz, K., Gunes, E., & Bas, A. (2021). CNN-based Gender Prediction in Uncontrolled Environments. Duzce University Journal of Science and Technology, 9(2), 890–898. https://doi.org/10.29130/dubited.763427
CR  - Yildiz, S. G., & Yildiz, K. (2023). Ann Based Early Detection of Alzheimer Disease on Selected Features. Journal of Engineering Sciences and Design, 11(4), 1508–1516. https://doi.org/10.21923/JESD.1296283
UR  - https://doi.org/10.17714/gumusfenbil.1714884
L1  - https://dergipark.org.tr/tr/download/article-file/4938536
ER  -