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ADNet: A CNN MODEL FOR ALZHEIMER'S DISEASE DIAGNOSIS ON OASIS-1 DATASET

Yıl 2025, Cilt: 28 Sayı: 1, 487 - 504, 03.03.2025

Ahmet Samed Saraçoğlu , Ayse Merve Acılar , Özlem Erdaş Çiçek

Öz

Alzheimer's disease (AD) is a chronic neurodegenerative disorder affecting memory, thinking, and behavior. Deep learning models, particularly CNNs, have shown promise in detecting AD at initial stages using the brain's magnetic resonance images (MRI). In this study, a CNN model called ADNet, trained using the OASIS-1 dataset, was proposed. The experimental approaches for evaluating the performance of ADNet are as follows: First, three different datasets were prepared using slices taken from the first quarter, middle, and third quarter of the sagittal plane from each MRI, to determine the most informative slice among the 128 slices. Each dataset was split into 80% training and 20% testing. It was found that the first quarter slice showed the best performance. The potential use of the obtained model as a transfer learning model was also examined. For this, a low-performance model was retrained using ADNet as a transfer learning model, and significant improvements in the results were observed. At last, the model’s robustness was evaluated in a more detailed evaluation, using 5-fold cross-validation repeated three times, resulting in a mean accuracy of 97.05%. As a result, ADNet can be used for Alzheimer's screening in clinical settings and could enable patients to receive earlier treatment.

Anahtar Kelimeler

Alzheimer deep learning convolutional neural network magnetic resonance imaging data transfer learning open access series of imaging studies brain database

Destekleyen Kurum

Coordinatorship of Scientific Research Projects of Necmettin Erbakan University

Proje Numarası

23GAP19015

Teşekkür

This study has been financially supported by the Coordinatorship of Scientific Research Projects of Necmettin Erbakan University [Project no: 23GAP19015].

Kaynakça

  • Afzal, S., Maqsood, M., Khan, U., Mehmood, I., Nawaz, H., Aadil, F., & Nam, Y. (2021). Alzheimer Disease Detection Techniques and Methods : A Review. 6, 26–38. https://doi.org/10.9781/ijimai.2021.04.005
  • Alroobaea, R., & Bragazzi, N. L. (2021). Alzheimer ’ s Disease Early Detection Using Machine Learning Techniques. 1–16.
  • Alzeimer’s Association. (2023). 2023 Alzheimer’s disease facts and figures. Alzheimer’s Dement., 19(4)(February), 1598–1695. https://doi.org/10.1002/alz.13016
  • Avots, E., Jafari, A., Ozcinar, C., & Anbarjafari, G. (2024). Comparative efficacy of histogram-based local descriptors and CNNs in the MRI-based multidimensional feature space for the differential diagnosis of Alzheimer’s disease: a computational neuroimaging approach. Signal, Image and Video Processing, 18(1), 1–13. https://doi.org/10.1007/s11760-023-02942-z
  • Baglat, P., Salehi, A. W., Gupta, A., & Gupta, G. (2020). Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset. In S. K. Sharma, Y. K. Dwivedi, B. Metri, & N. P. Rana (Eds.), Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology (Vol. 617, pp. 614–622). https://doi.org/10.1007/978-3-030-64849-7_54
  • 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. https://doi.org/10.1007/s13369-022-07538-2
  • Bendlin, B. B., Carlsson, C. M., Gleason, C. E., Johnson, S. C., Sodhi, A., Puglielli, L., … Wharton, W. (2011). Midlife predictors of Alzheimer’s disease. Maturitas, 65(2), 131–137. https://doi.org/10.1016/j.maturitas.2009.12.014.Midlife
  • Breijyeh, Z., & Karaman, R. (2020). Comprehensive Review on Alzheimer’s Disease : Causes and Treatment. Molecules, 25(5789), 1–28.
  • 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(1531), 1–14.
  • Ghosh, T., Palash, M. I. A., Yousuf, M. A., Hamid, M. A., Monowar, M. M., & Alassafi, M. O. (2023). A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images. Mathematics, 11(12), 2633. https://doi.org/10.3390/math11122633
  • Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Garcia, S., … I, B. L. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7(December), 1–13. https://doi.org/10.3389/fnins.2013.00267
  • Hajamohideen, F., Shaffi, N., Mahmud, M., Subramanian, K., Al Sariri, A., Vimbi, V., & Abdesselam, A. (2023). Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function. Brain Informatics, 10(1). https://doi.org/10.1186/s40708-023-00184-w
  • Helaly, H. A., Badawy, M., & Haikal, A. Y. (2022). Deep Learning Approach for Early Detection of Alzheimer ’ s Disease. Cognitive Computation, (September 2021), 1711–1727. https://doi.org/10.1007/s12559-021-09946-2
  • Jadhao, P., Palsodkar, P., Raut, R., Chaube, K., Rathod, D., & Palsodkar, P. (2023). Prediction of Early Stage Alzheimer ’ s using Machine Learning Algorithm. 2023 4th International Conference for Emerging Technology (INCET), 1–5. https://doi.org/10.1109/INCET57972.2023.10170583
  • Khagi, B., & Kwon, G. R. (2019). CNN model performance analysis on MRI images of an OASIS dataset for distinction between healthy and Alzheimer’s patients. IEIE Transactions on Smart Processing and Computing, 8(4), 272–278. https://doi.org/10.5573/IEIESPC.2019.8.4.272
  • Krüger, F. (2016). Activity, Context, and Plan Recognition with Computational Causal Behaviour Models. Faculty of Computer Science and Electrical Engineering, University of Rostock, Phd Thesis, p:71-72.
  • Lu, B., Li, H., Chang, Z., Li, L., Chen, N., Zhu, Z., & Zhou, H. (2022). A practical Alzheimer ’ s disease classifier via brain imaging-based deep learning on 85 , 721 samples.
  • Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., Data, C. M. R. I., … Buckner, R. L. (2007). Open Access Series of Imaging Studies ( OASIS ): Cross-sectional MRI Data in Young , Middle Aged , Nondemented , and Demented Older Adults Citation Open Access Series of Imaging Studies ( OASIS ): Nondemented , and Demented Older Adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498
  • Mendoza-Leon, R., Puentes, J., Felipe, L., & Hern, M. (2020). Single-slice Alzheimer ’ s disease classification and disease regional analysis with Supervised Switching Autoencoders. Computers in Biology and Medicine, 116(October 2019), 1–14. https://doi.org/10.1016/j.compbiomed.2019.103527
  • Mohammed, B. A., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A. A., Al-Mekhlafi, Z. G., … Ghaleb, F. A. (2021). Multi-method analysis of medical records and mri images for early diagnosis of dementia and alzheimer’s disease based on deep learning and hybrid methods. Electronics (Switzerland), 10(22). https://doi.org/10.3390/electronics10222860
  • Neffati, S., Ben Abdellafou, K., Jaffel, I., Taouali, O., & Bouzrara, K. (2019). An improved machine learning technique based on downsized KPCA for Alzheimer’s disease classification. International Journal of Imaging Systems and Technology, 29(2), 121–131. https://doi.org/10.1002/ima.22304
  • Ovsepian, S. V, Leary, V. B. O., Zaborszky, L., & Ntziachristos, V. (2019). HHS Public Access. 25(4), 288–297. https://doi.org/10.1177/1073858418791128.Amyloid
  • Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N., & Rubino, I. (2021). Diagnosis of Early Alzheimer’s Disease: Clinical Practice in 2021. 3(8), 371–386.
  • Rajayyan, S., & Mustafa, S. M. M. (2023). Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm. International Journal of Electrical and Computer Engineering, 13(4), 4623–4632. https://doi.org/10.11591/ijece.v13i4.pp4623-4632
  • Salami, F., Bozorgi-Amiri, A., Hassan, G. M., Tavakkoli-Moghaddam, R., & Datta, A. (2022). Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set. Biomedical Signal Processing and Control, 74(September 2021), 1–7. https://doi.org/10.1016/j.bspc.2022.103527
  • Salhi, S., Kora, Y., Ham, G., Zadeh, H., Id, H., & Simon, C. (2023). Network analysis of the human structural connectome including the brainstem. PLoS ONE, 18(4), 1–20. https://doi.org/10.1371/journal.pone.0272688
  • Saratxaga, C. L., Moya, I., Picón, A., Acosta, M., Moreno-Fernandez-de-leceta, A., Garrote, E., & Bereciartua-Perez, A. (2021). Mri deep learning-based solution for alzheimer’s disease prediction. Journal of Personalized Medicine, 11(9). https://doi.org/10.3390/jpm11090902
  • Scheltens, P., Strooper, B. De, Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., … Flier, W. M. Van Der. (2022). Alzheimer ’ s disease. 397(10284), 1577–1590. https://doi.org/10.1016/S0140-6736(20)32205-4.Alzheimer
  • Shrivastava, R. K., Singh, S. P., & Kaur, G. (2023). shrivastava.pdf. In D. Koundal, D. K. Jain, Y. Guo, A. S. Ashour, & A. Zaguia (Eds.), Data Analysis for Neurodegenerative Disorders. Cognitive Technologies. (pp. 111–126). https://doi.org/10.1007/978-981-99-2154-6_6

ADNet: OASIS-1 VERİ KÜMESİ ÜZERİNDE ALZHEİMER HASTALIĞI TEŞHİSİ İÇİN BİR CNN MODELİ

Yıl 2025, Cilt: 28 Sayı: 1, 487 - 504, 03.03.2025

Ahmet Samed Saraçoğlu , Ayse Merve Acılar , Özlem Erdaş Çiçek

Öz

Alzheimer hastalığı (AD), hafıza, düşünme ve davranış üzerinde ciddi etkileri olan kronik bir nörodejeneratif hastalıktır. Evrişimli Sinir Ağları (CNN) gibi derin öğrenme modelleri, beyin manyetik rezonans görüntüleri (MRI) kullanılarak AD'nin erken aşamalarda tespit edilmesinde umut verici sonuçlar göstermektedir. Bu çalışmada, Alzheimer teşhisi için OASIS-1 veri seti kullanılarak eğitilen ADNet adlı bir CNN modeli önerilmiştir. ADNet'in performansını değerlendirmek için, ilk olarak, her bireyin MR görüntüsünden alınan sagittal düzlemdeki 128 dilimin ilk çeyreğinden, ortasından ve üçüncü çeyreğinden alınan dilimler kullanılarak üç farklı veri seti hazırlanmış ve en bilgilendirici dilim hangisi araştırılmıştır. Her veri seti %80 eğitim ve %20 test olarak ayrılmış ve ilk çeyrek dilimin en iyi performansı gösterdiği saptanmıştır. Ek olarak, elde edilen modelin transfer öğrenme modeli olarak kullanılıp kullanılamayacağı incelenmiştir. Bunun için düşük performanslı bir model, ADNet transfer öğrenme modeli kullanılarak yeniden eğitilmiş ve sonuçların oldukça iyileştiği gözlemlenmiştir. Son olarak, modelin dayanıklılığı 5 katlı çapraz doğrulama ile üç kez tekrarlanarak daha ayrıntılı bir değerlendirmeye tabi tutulmuş ve %95,36 ortalama doğruluk elde edilmiştir. Sonuç olarak, ADNet’in klinik ortamlarda Alzheimer taramasında kullanılabileceği ve hastaların daha erken tedavi almasını sağlayabileceği düşünülmektedir.

Anahtar Kelimeler

Alzheimer hastalığı teşhisi derin öğrenme evrişimli sinir ağı manyetik rezonans görüntüleme verisi transfer öğrenme açık eri̇şi̇mli̇ görüntüleme çalışmaları seri̇si̇ beyi̇n veri̇ tabanı

Destekleyen Kurum

Necmettin Erbakan Üniversitesi BAP Koordinatörlüğü

Proje Numarası

23GAP19015

Teşekkür

Bu çalışma Necmettin Erbakan Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü tarafından maddi olarak desteklenmiştir [Proje no: 23GAP19015].

Kaynakça

  • Afzal, S., Maqsood, M., Khan, U., Mehmood, I., Nawaz, H., Aadil, F., & Nam, Y. (2021). Alzheimer Disease Detection Techniques and Methods : A Review. 6, 26–38. https://doi.org/10.9781/ijimai.2021.04.005
  • Alroobaea, R., & Bragazzi, N. L. (2021). Alzheimer ’ s Disease Early Detection Using Machine Learning Techniques. 1–16.
  • Alzeimer’s Association. (2023). 2023 Alzheimer’s disease facts and figures. Alzheimer’s Dement., 19(4)(February), 1598–1695. https://doi.org/10.1002/alz.13016
  • Avots, E., Jafari, A., Ozcinar, C., & Anbarjafari, G. (2024). Comparative efficacy of histogram-based local descriptors and CNNs in the MRI-based multidimensional feature space for the differential diagnosis of Alzheimer’s disease: a computational neuroimaging approach. Signal, Image and Video Processing, 18(1), 1–13. https://doi.org/10.1007/s11760-023-02942-z
  • Baglat, P., Salehi, A. W., Gupta, A., & Gupta, G. (2020). Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset. In S. K. Sharma, Y. K. Dwivedi, B. Metri, & N. P. Rana (Eds.), Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology (Vol. 617, pp. 614–622). https://doi.org/10.1007/978-3-030-64849-7_54
  • 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. https://doi.org/10.1007/s13369-022-07538-2
  • Bendlin, B. B., Carlsson, C. M., Gleason, C. E., Johnson, S. C., Sodhi, A., Puglielli, L., … Wharton, W. (2011). Midlife predictors of Alzheimer’s disease. Maturitas, 65(2), 131–137. https://doi.org/10.1016/j.maturitas.2009.12.014.Midlife
  • Breijyeh, Z., & Karaman, R. (2020). Comprehensive Review on Alzheimer’s Disease : Causes and Treatment. Molecules, 25(5789), 1–28.
  • 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(1531), 1–14.
  • Ghosh, T., Palash, M. I. A., Yousuf, M. A., Hamid, M. A., Monowar, M. M., & Alassafi, M. O. (2023). A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images. Mathematics, 11(12), 2633. https://doi.org/10.3390/math11122633
  • Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Garcia, S., … I, B. L. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7(December), 1–13. https://doi.org/10.3389/fnins.2013.00267
  • Hajamohideen, F., Shaffi, N., Mahmud, M., Subramanian, K., Al Sariri, A., Vimbi, V., & Abdesselam, A. (2023). Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function. Brain Informatics, 10(1). https://doi.org/10.1186/s40708-023-00184-w
  • Helaly, H. A., Badawy, M., & Haikal, A. Y. (2022). Deep Learning Approach for Early Detection of Alzheimer ’ s Disease. Cognitive Computation, (September 2021), 1711–1727. https://doi.org/10.1007/s12559-021-09946-2
  • Jadhao, P., Palsodkar, P., Raut, R., Chaube, K., Rathod, D., & Palsodkar, P. (2023). Prediction of Early Stage Alzheimer ’ s using Machine Learning Algorithm. 2023 4th International Conference for Emerging Technology (INCET), 1–5. https://doi.org/10.1109/INCET57972.2023.10170583
  • Khagi, B., & Kwon, G. R. (2019). CNN model performance analysis on MRI images of an OASIS dataset for distinction between healthy and Alzheimer’s patients. IEIE Transactions on Smart Processing and Computing, 8(4), 272–278. https://doi.org/10.5573/IEIESPC.2019.8.4.272
  • Krüger, F. (2016). Activity, Context, and Plan Recognition with Computational Causal Behaviour Models. Faculty of Computer Science and Electrical Engineering, University of Rostock, Phd Thesis, p:71-72.
  • Lu, B., Li, H., Chang, Z., Li, L., Chen, N., Zhu, Z., & Zhou, H. (2022). A practical Alzheimer ’ s disease classifier via brain imaging-based deep learning on 85 , 721 samples.
  • Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., Data, C. M. R. I., … Buckner, R. L. (2007). Open Access Series of Imaging Studies ( OASIS ): Cross-sectional MRI Data in Young , Middle Aged , Nondemented , and Demented Older Adults Citation Open Access Series of Imaging Studies ( OASIS ): Nondemented , and Demented Older Adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498
  • Mendoza-Leon, R., Puentes, J., Felipe, L., & Hern, M. (2020). Single-slice Alzheimer ’ s disease classification and disease regional analysis with Supervised Switching Autoencoders. Computers in Biology and Medicine, 116(October 2019), 1–14. https://doi.org/10.1016/j.compbiomed.2019.103527
  • Mohammed, B. A., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A. A., Al-Mekhlafi, Z. G., … Ghaleb, F. A. (2021). Multi-method analysis of medical records and mri images for early diagnosis of dementia and alzheimer’s disease based on deep learning and hybrid methods. Electronics (Switzerland), 10(22). https://doi.org/10.3390/electronics10222860
  • Neffati, S., Ben Abdellafou, K., Jaffel, I., Taouali, O., & Bouzrara, K. (2019). An improved machine learning technique based on downsized KPCA for Alzheimer’s disease classification. International Journal of Imaging Systems and Technology, 29(2), 121–131. https://doi.org/10.1002/ima.22304
  • Ovsepian, S. V, Leary, V. B. O., Zaborszky, L., & Ntziachristos, V. (2019). HHS Public Access. 25(4), 288–297. https://doi.org/10.1177/1073858418791128.Amyloid
  • Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N., & Rubino, I. (2021). Diagnosis of Early Alzheimer’s Disease: Clinical Practice in 2021. 3(8), 371–386.
  • Rajayyan, S., & Mustafa, S. M. M. (2023). Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm. International Journal of Electrical and Computer Engineering, 13(4), 4623–4632. https://doi.org/10.11591/ijece.v13i4.pp4623-4632
  • Salami, F., Bozorgi-Amiri, A., Hassan, G. M., Tavakkoli-Moghaddam, R., & Datta, A. (2022). Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set. Biomedical Signal Processing and Control, 74(September 2021), 1–7. https://doi.org/10.1016/j.bspc.2022.103527
  • Salhi, S., Kora, Y., Ham, G., Zadeh, H., Id, H., & Simon, C. (2023). Network analysis of the human structural connectome including the brainstem. PLoS ONE, 18(4), 1–20. https://doi.org/10.1371/journal.pone.0272688
  • Saratxaga, C. L., Moya, I., Picón, A., Acosta, M., Moreno-Fernandez-de-leceta, A., Garrote, E., & Bereciartua-Perez, A. (2021). Mri deep learning-based solution for alzheimer’s disease prediction. Journal of Personalized Medicine, 11(9). https://doi.org/10.3390/jpm11090902
  • Scheltens, P., Strooper, B. De, Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., … Flier, W. M. Van Der. (2022). Alzheimer ’ s disease. 397(10284), 1577–1590. https://doi.org/10.1016/S0140-6736(20)32205-4.Alzheimer
  • Shrivastava, R. K., Singh, S. P., & Kaur, G. (2023). shrivastava.pdf. In D. Koundal, D. K. Jain, Y. Guo, A. S. Ashour, & A. Zaguia (Eds.), Data Analysis for Neurodegenerative Disorders. Cognitive Technologies. (pp. 111–126). https://doi.org/10.1007/978-981-99-2154-6_6
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Örüntü Tanıma, Derin Öğrenme, Nöral Ağlar
Bölüm Bilgisayar Mühendisliği
Yazarlar

Ahmet Samed Saraçoğlu 0009-0003-7835-4191

Ayse Merve Acılar 0000-0002-0133-2694

Özlem Erdaş Çiçek 0000-0003-4019-7744

Proje Numarası 23GAP19015
Yayımlanma Tarihi 3 Mart 2025
Gönderilme Tarihi 20 Ağustos 2024
Kabul Tarihi 14 Kasım 2024
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 1

Kaynak Göster

APA Saraçoğlu, A. S., Acılar, A. M., & Erdaş Çiçek, Ö. (2025). ADNet: A CNN MODEL FOR ALZHEIMER’S DISEASE DIAGNOSIS ON OASIS-1 DATASET. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 487-504.
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