Murat Avşar, Kemal Polat*
Kemal Polat
Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey
* Corresponding Author: Kemal Polat, Email: [email protected]
Alzheimer's disease is a significant disease that negatively affects daily life and reduces the quality of human life. Dementia and Alzheimer's disease occur as the loss of neurons or a decrease in the relationship between neurons. So far, no effective drug has been found in diagnosing this disease. For this reason, it has become essential for individuals to diagnose the disease early and to detect the disease before it progresses. However, early diagnosis of the disease is challenging. The disease can be diagnosed after significant and irreversible effects on humans occur. A lot of research has been done worldwide for early disease diagnosis. Deep learning algorithms have become essential in diagnosing this disease. Significant progress has been made in diagnosing the disease with models created using deep learning algorithms. This study used a sequential model, conv2D, maxPooling2D, and dense layers to diagnose and classify. According to the dataset from Kaggle, a 4-class dataset has been used in this study to diagnose Alzheimer's disease. According to the Alzheimer's MRI dataset, the disease has been classified as nondemented, moderate demented, mild demented, and very mild demented, respectively.
The proposed model has been trained using CNN. The number of layers and dropout rate have been used as performance metrics. In our study, activation Leaky ReLU was used. The SMOTE technique has been used to oversample the available data. This study's classification results will help experts make the right decisions. With F1Score, accuracy, recall, and precision values, 96.35% success was achieved in the CNN model. Different CNN methods can be used to advance these studies.
MRI imaging, Convolutional neural network, Alzheimer's disease recognition, SMOTE technique, deep learning model
Murat Avşar, Kemal Polat (2023). Classifying Alzheimer's disease based on a convolutional neural network with MRI images. Journal of Artificial Intelligence and Systems, 5, 46–57. https://doi.org/10.33969/AIS.2023050104.
[1] Wee, C. Y., Yap, P. T., & Shen, D. (2012). Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping, 34(12), 3411–3425. https://doi.org/10.1002/hbm.22156
[2] World Alzheimer Report 2018—The State of the Art of Dementia Research: New Frontiers. New Frontiers; Alzheimer's Disease International: London, UK, 2018; Volume 48.
[3] Aldhyani T. H. H., Alrasheedi M., Alqarni A. A., Alzahrani M. Y., Bamhdi A. M. Intelligent hybrid model to enhance time series models for predicting network traffic. IEEE Access. 2020;8:130431–130451. doi: 10.1109/ACCESS.2020.3009169. [CrossRef] [Google Scholar] [Ref list]
[4] Litjens G., Kooi T., Bejnordi B. E., et al. A survey on deep learning in medical image analysis. Medical Image Analysis. 2017;42:60–88. doi: 10.1016/j.media.2017.07.005. [PubMed] [CrossRef] [Google Scholar]
[5] Alom M. Z., Taha T. M., Yakopcic C., et al. A state-of-the-art survey on deep learning theory and architectures. Electronics. 2019;8(3):p. 292. doi: 10.3390/electronics8030292. [CrossRef] [Google Scholar]
[6] Horikawa T., Tamaki M., Miyawaki Y., Kamitani Y. Neural decoding of visual imagery during sleep. Science. 2013;340(6132):639–642. doi: 10.1126/science.1234330. [PubMed] [CrossRef] [Google Scholar]
[7] Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An Introductory Review of Deep Avrupa Bilim ve Teknoloji Dergisi e-ISSN: 2148-2683 130 Learning for Prediction Models With Big Data. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00004
[8] Qiu S., Chang G. H., Panagia M., Gopal D. M., Au R., Kolachalama V. B. Fusion of deep learning models of MRI scans, mini–mental state examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 2018;10(1):737–749. doi: 10.1016/j.dadm.2018.08.013. [PMC free article] [PubMed] [CrossRef] [Google Scholar] [Ref list]
[9] Gu J., Wang Z., Kuen J., et al. Recent advances in convolutional neural networks. Pattern Recognition. 2018;77:354–377. doi: 10.1016/j.patcog.2017.10.013. [CrossRef] [Google Scholar] [Ref list]
[10] Haohan W., Bhiksha R. On the origin of deep learning. 2017. https://arxiv.org/abs/1702.07800. [Ref list]
[11] Mahdi A. M. Carbondale, USA: Ph. D. dissertation, Dept. ECE, Southern Illinois Univ.; 2019. Visual saliency analysis, prediction, and visualization: a deep learning perspective. [Google Scholar] [Ref list]
[12] Flatteningmethod https://rubikscode.net/2018/02/26/introduction-to-convolutional-neural-networks/
[13] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." International conference on machine learning. PMLR, 2015.
[14] Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research 15.1 (2014): 1929-1958.
[15] Tensorflow library https://www.tensorflow.org/, (last accessed: March, 2023).
[16] Seaborn library https://seaborn.pydata.org/, (last accessed: March, 2023).
[17] Numpy library https://numpy.org/, (last accessed: March, 2023).
[18] Pandas library https://pandas.pydata.org/, (last accessed: March, 2023).
[19] Matplotlib library https://pandas.pydata.org/, (last accessed: March, 2023).
[20] Keras library https://keras.io/, (last accessed: March, 2023).
[21] PIL library https://pillow.readthedocs.io/en/stable/reference/Image.html, (last accessed: March, 2023).
[22] Smote https://imbalancedlearn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html, (last accessed: March, 2023).
[23] Scikit learn library https://scikit-learn.org/stable/, (last accessed: March, 2023).
[24] Aldhyani T. H. H., Alshebami A. S., Alzahrani M. Y. Soft clustering for enhancing the diagnosis of chronic diseases over machine learning algorithms. Journal of Healthcare Engineering. 2020;2020:16. doi: 10.1155/2020/4984967.4984967 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
[25] Mohamed A. M. A., Uçan O. N., Bayat O., Duru A. D. Classification of resting-state status based on sample entropy and power spectrum of electroencephalography (EEG) Applied Bionics and Biomechanics. 2020;2020:10. doi: 10.1155/2020/8853238.8853238 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
[26] LaMontagne P. J., Benzinger T. L. S., Morris J. C., et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. med Rxiv. 2019 doi: 10.1101/2019.12.13.19014902. [CrossRef] [Google Scholar]
[27] Herrick R., Horton W., Olsen T., McKay M., Archie K. A., Marcus D. S. XNAT Central: open sourcing imaging research data. NeuroImage. 2016;124(Part B):1093–1096. doi: 10.1016/j.neuroimage.2015.06.076. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
[28] Lee, G., Nho, K., Kang, B., Sohn, K.A., Kim, D. (2019). Predicting Alzheimer's disease progression using multimodal deep learning approach. Scientific Reports, 9(1): 1-12. https://doi.org/10.1038/s41598-018-37769-z.
[29] Gao, X.W., Hui, R., Tian, Z. (2017). Classification of CT brain images based on deep learning networks. Computer Methods and Programs in Biomedicine, 138: 49-56. https://doi.org/10.1016/j.cmpb.2016.10.007Li, F., Cheng, D., & Liu, M. "Alzheimer's Disease Classication Based on Combination of Multi-model Convolutional Networks"(2021).
[30] Ortiz A., Munilla, J., Gorriz, J.M., Ramirez, J. (2016). Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease. International journal of Neural Systems, 26(7): 1650025. https://doi.org/10.1142/S0129065716500258
[31] Yildirim M, Çinar A. (2020) Classification of Alzheimer's disease MRI images with CNN based hybrid method Ingénierie des Systèmes D Information 25(4):413-418 DOI: 10.18280/isi.250402