Derin öğrenme yöntemleri kullanılarak gradyan tabanlı MR görüntülerinde erken safha Alzheimer hastalığının tespiti

Yıl 2024, Cilt: 13 Sayı: 3

Mehmet Emre Sertkaya Meryem Durmuş Burhan Ergen

https://doi.org/10.28948/ngumuh.1390830

Öz

Alzheimer Hastalığı (AD), genellikle yaşlılık döneminde görülen bir demans türüdür. AD, beynin sinir hücrelerine yavaşça zarar veren bir nörodejeneratif hastalıktır. Erken evre AD belirtileri genellikle hafif ve tipik olmayan olduğundan teşhisi zorlaştırır. Ancak erken teşhis, hastalığın ilerlemesini yavaşlatma ve uygun tedavi seçenekleri sunma açısından müdahaleyi mümkün kılar. Derin öğrenme yöntemleri, özellikle Manyetik Rezonans (MR) görüntülerinde ince detayları tespit etmek için kullanılabilir. Bu yöntemler, özellikle Manyetik Rezonans Görüntüleme (MRG) gibi görüntüleme tekniklerinde, çeşitli yapıların konumunu, şeklini ve boyutunu belirlemede gradient tabanlı görüntülerin önemli bir rol oynadığı düşünülmektedir. Bu çalışma, farklı açılardaki ve sabit yoğunluktaki beyin MR görüntülerini eğitmek için Derin Öğrenme modellerine önce Gradient filtresi uygulayarak Alzheimer hastalığının erken tespiti konusundaki modellerin performansını artırmayı amaçlamaktadır. Erken aşama AD'yi temsil eden üç farklı kategoriden oluşan görüntü veri seti, hafif bilişsel bozukluğa sahip bireylerin ve sağlıklı bireylerin görüntülerini içermektedir. Orijinal ve gradient filtrelenmiş görüntü alt kümeleri, derin öğrenme modellerine giriş olarak kullanılmıştır. Çalışma sonuçları, gradient tabanlı görüntülerin derin öğrenme modellerinde orijinal görüntülerden daha iyi bir performans sergilediğini göstermektedir. Densenet201 derin öğrenme modeli %98,63'lük en yüksek doğruluk oranını elde etmiştir.

Anahtar Kelimeler

Alzheimer Hastalığı, Derin Öğrenme Teknikleri, Gradyan Filtre, Manyetik Rezonans Görüntüleri, Hafif Bilişsel Bozukluk

Detection of early stage Alzheimer's disease in gradient-based MR images using deep learning methods

Öz

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of Mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

Anahtar Kelimeler

Alzheimer, Deep Learning Techniques, Gradient Filter, Magnetic Resonance Images, Mild Cognitive Impairment

Kaynakça

• Z. Wang, et al., Classification of Alzheimer’s disease, mild cognitive impairment and normal control subjects using resting-state fMRI based network connectivity analysis. IEEE journal of translational engineering in health and medicine, 6, 1-9, 2018. https://doi.org/ 10.1109/JTEHM.2018.2874887
• S. Devkota, T.D. Williams, M.S.J.J. Wolfe, Familial Alzheimer’s disease mutations in amyloid protein precursor alter proteolysis by γ-secretase to increase amyloid β-peptides of≥ 45 residues. Journal of Biological Chemistry, 296, 2021. https://doi.org/ 10.1016/j.jbc.2021.100281
• F. Feng, et al., Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment. Frontiers in aging neuroscience, 10, 290, 2018. https://doi.org/10.3389/fnagi.2018.0 0290
• Chen, X., Li, L., Sharma, A., Dhiman, G., & Vimal, S., The application of convolutional neural network model in diagnosis and nursing of MR imaging in Alzheimer's disease. Interdisciplinary Sciences: Computational Life Sciences, 14 (1), 34-44, 2022. https://doi.org/https:/ 10.1007/s12539-021-00450-7
• Sarraf, S., & Tofighi, G., Classification of Alzheimer's disease using fMRI data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631, 2016. https://doi.org/10.485 50/arXiv.1603.08631
• Q. Zhou, et al., An optimal decisional space for the classification of Alzheimer's disease and mild cognitive impairment. IEEE Transactions on Biomedical Engineering, 61 (8), 2245-2253, 2014. https://doi.org/ 10.1109/TBME.2014.2310709
• G. Chen, et al., Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology, 259 (1), 213, 2011. https://doi.org/10.1148/radiol.10100734
• N. Yamanakkanavar, J.Y. Choi, B.J.S. Lee, MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors, 20 (11), 3243, 2020. https://doi.org/ 10.3390/s20113243
• R. Smith-Bindman, et al., Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010. Jama, 307(22), 2400-2409, 2012. https://doi.org/10.1001/jama.2012.5960
• F. Ramzan, et al., A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. Journal of medical systems, 44 (2), 1-16, 2020. https://doi.org/10.10 07/s10916-019-1475-2
• H.A. Helaly, M. Badawy, A.Y. Haikal, Deep learning approach for early detection of Alzheimer’s disease. Cognitive Computation, 14 (5), 1711-1727, 2022. https://doi.org/10.1007/s12559-021-09946-2
• Mehmood, A., Maqsood, M., Bashir, M., & Shuyuan, Y., A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain sciences, 10 (2), 84, 2020. https://doi.org/10.3390 /brainsci10020084
• M. Mujahid, et al., An efficient ensemble approach for Alzheimer’s disease detection using an adaptive synthetic technique and deep learning. Diagnostics, 13 (15), 2489, 2023. https://doi.org/10.3390/diagnostics 13152489
• A. Farooq, et al. A deep CNN based multi-class classification of Alzheimer's disease using MRI. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, 1-6, 2017. https://doi.org/10.1109/IST.2017.8261460
• R. Ibrahim, R. Ghnemat, Q. Abu Al-Haija, Improving Alzheimer’s disease and brain tumor detection using deep learning with particle swarm optimization. AI, 4(3), 551-573, 2023. https://doi.org/10.3390/ai40300 30
• P. Balaji, et al., Hybridized deep learning approach for detecting Alzheimer’s disease. Biomedicines, 11(1), 149, 2023. https://doi.org/10.3390/biomedicines11010 149
• S. Basaia, et al., Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical, 21, 101645, 2019. https://doi.org/10.1016/j.nicl.2018.101645
• S. Liu, et al. Early diagnosis of Alzheimer's disease with deep learning. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), IEEE, 2014. https://doi.org/10.1109/ISBI.20 14.6868045
• Kaggle. Alzheimer-MRI|Kaggle. Available from: https://www.kaggle.com/datasets/phamnguyenduytien/alzheimermri, July 10, 2022.
• C.C. Aggarwal, Neural networks and deep learning. Springer, 10(978), 3, 2018.
• S. Sharma, S. Sharma, A. Athaiya, Activation functions in neural networks. Towards Data Science, 6 (12), 310-316, 2017. https://doi.org/10.33564/IJEAST. 2020.v04i12.054
• L. Zhou, et al., Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361, 2017. https://doi.org/10.1016/j.neucom.2017. 01.026
• S.H. Lee, et al., How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 71, 1-13, 2017. https://doi.org/10.1016/ j.patcog.2017.05.015
• M.D. Zeiler, G.W. Taylor, R. Fergus. Adaptive deconvolutional networks for mid and high level feature learning. Proceedings of the 2011 International Conference on Computer Vision, IEEE, 2011. https://doi.org/10.1109/ICCV.2011.6126474
• A. Apicella, et al., A survey on modern trainable activation functions. Neural Networks, 138, 14-32, 2021. https://doi.org/10.1016/j.neunet.2021.01.026
• P. Wang, E. Fan, P. Wang, Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141, 61-67, 2021. https:// doi.org/10.1016/j.patrec.2020.07.042
• S. Jia, et al., A survey: Deep learning for hyperspectral image classification with few labeled samples. Neurocomputing, 448, 179-204, 2021. https://doi.org/ 10.1016/j.neucom.2021.03.035
• U. Ruby, V. Yendapalli, Binary cross entropy with deep learning technique for image classification. International Journal of Advanced Trends in Computer Science and Engineering, 9 (10), 2020. https://doi. org/10.30534/ijatcse/2020/175942020
• D. Theckedath, R. Sedamkar, Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Science, 1, 1-7, 2020. https://doi.org/ 10.1007/s42979-020-0114-9
• A. Karacı, VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm. Neural Computing and Applications, 34(10), 8253-8274, 2022. https://doi.org/10.1007/s00521-022-069 18-x
• M. Yildirim, A. Çinar, E. Cengil, Classification of the weather images with the proposed hybrid model using deep learning, SVM classifier, and mRMR feature selection methods. Geocarto International, 37 (9), 2735-2745, 2022. https://doi.org/10.1080/1010604 9.2022.034989
• A. Jaiswal, et al., Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics, 39 (15), 5682-5689, 2021. https://doi.org/10.1080/07391102.2020.1788642
• J.C. Koh, G. Spangenberg, S. Kant, Automated machine learning for high-throughput image-based plant phenotyping. Remote Sensing, 13(5), 858, 2021. https://doi.org/10.3390/rs13050858
• A.G. Howard, et al., Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
• H. Lu, et al., FDCNet: filtering deep convolutional network for marine organism classification. Multimedia Tools and Applications, 77, 21847-21860, 2018. https://doi.org/10.1007/s11042-017-4585-1
• A. Shabbir, et al., Satellite and scene image classification based on transfer learning and fine tuning of ResNet50. Mathematical Problems in Engineering, 2021, 1-18, 2021. https://doi.org/10.1155/2021/5843816
• M. Toğaçar, Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images. Computer Methods and Programs in Biomedicine, 214, 106579, 2022. https://doi.org/ 10.1016/j.cmpb.2021.106579
• J. Na'am, et al., Filter technique of medical image on multiple morphological gradient (MMG) method. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17 (3), 1317-1323, 2019. https://doi.org/10.12928/telkomnika.v17i3.9722
• M. Nakashizuka, Image regularization with multiple morphological gradient priors. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), IEEE, 2016. https://doi.org/10.1109/ICIP.20 16.7532973
• M. TOĞAÇAR, B. ERGEN, M.E. SERTKAYA, Zatürre hastalığının derin öğrenme modeli ile tespiti. Firat University Journal of Engineering, 31 (1), 223-230, 2019.