فصلنامه علمی کارافن

فصلنامه علمی کارافن

یک روش شبکه عصبی پیچشی عمیق سبک و دقیق مبتنی برMRI برای تشخیص و طبقه‌بندی بیماری آلزایمر

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان
1 استادیار، دانشکده مهندسی برق، دانشگاه تفرش، تفرش، ایران.
2 دانش آموخته کارشناسی ارشد، دانشگاه شهاب دانش، قم، ایران.
3 استادیار، گروه مهندسی برق، دانشگاه ملی مهارت (NUS)، تهران، ایران.
چکیده
هدف بیماری آلزایمر، یک اختلال پیشرونده مغزی است که تشخیص به‌موقع آن برای مدیریت و درمان مؤثر ضروری است. این مطالعه یک شبکه عصبی پیچشی[1] (CNN) کارآمد، کم‌حجم و دقیق را برای طبقه‌بندی تصاویر MRI مغز به چهار دسته مرتبط با آلزایمر ارائه می‌دهد. دو رویکرد اصلی برای طراحی روش در زمینه بهبود تشخیص، استفاده شده است: (1) پیش‌پردازش بهینه داده‌ها و (2) طراحی یک معماری سبک، کم‌حجم و دارای پیچیدگی کم شبکه عصبی پیچشی که به‌طور هم‌زمان دارای دقت خوب، کارایی محاسباتی و عملکرد عالی می­باشد. مدل پیشنهادی به‌دقت 22/99 درصد، میانگین امتیاز F1 برابر با 99/0، ضریب همبستگی متیو[2] (MCC) برابر با 9870/0 و ضریب کاپا کوهن[3] (CKC) برابر با 9870/0 دست یافت. در این تحقیق، علاوه بر دقت، پیچیدگی مدل پیشنهادی، مقایسه اندازه مدل، زمان سپری‌شده، عملیات نقطه شناور در ثانیه[4] (FLOPs) و پارامترهای قابل‌آموزش و غیرقابل‌آموزش روش پیشنهادی به‌طور کامل موردبررسی قرار گرفته است. این مدل با داشتن مزایای دقت بالا، کاهش FLOPs، زمان اجرای سریع‌تر و نیاز به حافظه کمتر، در مقایسه با دیگر روش‌های یادگیری عمیق استفاده‌شده در مطالعات اخیر، عملکرد بهتری دارد.
 
[1] Convolutional Neural Network
[2] Matthews correlation coefficient
[3] Cohen's kappa coefficient
[4] Floating-point operations per second
 
کلیدواژه‌ها
موضوعات

عنوان مقاله English

A Lightweight and Accurate Deep Convolutional Neural Network Method based on MRI for the Diagnosis and Classification of Alzheimer's Disease

نویسندگان English

Elahe Abdolkarimi 1
Yasaman Bakhtiari 2
Mohammad Reza Yazdani 3
1 Assistant Professor, Department of Electrical Engineering, Tafresh University, Tafresh, Iran.
2 Master's Graduate from Shahab Danesh University, Qom, Iran.
3 Assistant Professor, Department of Electrical Engineering, National University of Skill (NUS), Tehran, Iran.
چکیده English

The Alzheimer's disease, a progressive brain disorder, necessitates timely detection for effective management due to the current diagnostic methods' limitations. The study presents an efficient convolutional neural network (CNN) designed for classifying brain magnetic resonance imaging (MRI) images into four categories related to Alzheimer's disease. To enhance diagnosis, this study proposes two distinct approaches at different stages: (1) using optimized data pre-processing; and (2) designing a lightweight CNN architecture with low complexity and fewer parameters that simultaneously possesses good accuracy, computational efficiency, and excellent performance. The proposed method achieved outstanding results, with a final accuracy of 99.22%, a macro average F1 score of 0.99, an MCC of 0.9870, and a Cohen kappa score (CKS) of 0.9870. In addition to accuracy, the complexity of the proposed model, including the comparison of model size, time elapsed, the number of FLOPs, and the trainable and non-trainable parameters of the proposed method were also thoroughly investigated. This model, with the advantages of high accuracy, reduced FLOPs, faster execution time, and lower memory requirements outperforms other deep learning methods used in recent studies. 

کلیدواژه‌ها English

Alzheimer's Disease
Diagnosis
Convolutional Neural Network (CNN)
Brain MRI Images
Classification
Performance Evaluation
[1] Brookmeyer, R., Johnson, E., Ziegler-Graham, K., & Arrighi, H. M. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimer's & Dementia, 3(3), 186-191. https://doi.org/10.1016/j.jalz.2007.04.381
[2] Aparna, M., & Rao, B. S. (2023). Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease. Computers, Materials & Continua, 74(3), 6909-6932. https://doi.org/10.32604/cmc.2023.034796
[3] Dolz, J., Desrosiers, C., & Ben Ayed, I. (2018). 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study. NeuroImage, 170, 456-470. https://doi.or g/10.1016/j.neuroimage.2017.04.039
[4] 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(3), 103527. https://doi.org/10.10 16/j.bspc.2022.103527
[5] Patil, V., Madgi, M., & Kiran, A. (2022). Early prediction of Alzheimer's disease using convolutional neural network: a review. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 58(1), 130. https://doi.org/10.1186/s41983-022-00571-w
[6] AbdulAzeem, Y., Bahgat, W. M., & Badawy, M. (2021). A CNN based framework for classification of Alzheimer’s disease. Neural Computing and Applications, 33(16), 10415-10428. https://doi.org/10.1007/s00521-021-05799-w
[7] Mujahid, M., Rehman, A., Alam, T., Alamri, F. S., Fati, S. M., & Saba, T. (2023). An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning. Diagnostics, 13(15), 2489. https://doi.org/10.3390/d iagnostics13152489
[8] Pruthviraja, D., Nagaraju, S. C., Mudligiriyappa, N., Raisinghani, M. S., Khan, S. B., Alkhaldi, N. A., & Malibari, A. A. (2023). Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm. Diagnostics, 13(16), 2687. https://doi.org/10.3390/ diagnostics13162687
[9] Liu, S., Masurkar, A. V., Rusinek, H., Chen, J., Zhang, B., Zhu, W., Fernandez-Granda, C., & Razavian, N. (2022). Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs. Scientific Reports, 12(1), 17106. https://doi.org/10. 1038/s41598-022-20674-x
[10] Balaji, P., Chaurasia, M. A., Bilfaqih, S. M., Muniasamy, A., & Alsid, L. E. G. (2023). Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease. Biomedicines, 11(1), 149. https://doi.org/10.3390/biomedicines11010149
[11] Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M-O., Chupin, M., Benali, H., & Colliot, O. (2011). Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage, 56(2), 766-781. https://doi.org/10.1016/j.neuroimage.2010.06.013
[12] Davatzikos, C., Bhatt, P., Shaw, L. M., Batmanghelich, K. N., & Trojanowski, J. Q. (2011). Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging, 32(12), 2322.e19-2322.e27. https://doi.org/10.1016/j.neuro biolaging.2010.05.023
[13] Suk, H-I., 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
[14] Wee, C-Y., Yap, P-T., Li, W., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L., & Shen, D. (2011). Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage, 54(3), 1812-1822. https://doi. org/10.1016/j.neuroimage.2010.10.026
[15] Zhang, D., & Shen, D. (2012). Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one, 7(3), e33182. https://doi.org/10. 1371/journal.pone.0033182
[16] Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 101(13), 4637-4642. https:/ /doi.org/10.1073/pnas.0308627101
[17] Zhou, L., Wang, Y., Li, Y., Yap, P-T., & Shen, D. (2011). Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures. PloS one, 6(7), e21935. https://doi.org/10.1371/journal.pone.0021935
[18] Gray, K. R., Wolz, R., Heckemann, R. A., Aljabar, P., Hammers, A., & Rueckert, D. (2012). Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease. NeuroImage, 60(1), 221-229. https://doi.org/10.1016/j.neuroimage.2011.1 2.071
[19] Suk, H-I., Wee, C-Y., & Shen, D. (2013). Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification. In G. Wu, D. Zhang, D. Shen, P. Yan, K. Suzuki, & F. Wang (Eds.), Machine Learning in Medical Imaging (pp. 131-138). Springer International Publishing. https://doi.org/10.1007/978-3-319-02267-3_17
[20] Chen, Y. J., Deutsch, G., Satya, R., Liu, H-G., & Mountz, J. M. (2013). A semi-quantitative method for correlating brain disease groups with normal controls using SPECT: Alzheimer's disease versus vascular dementia. Computerized Medical Imaging and Graphics, 37(1), 40-47. https://doi.org/10.1016/j.compmedimag.2012.11.001
[21] Górriz, J. M., Segovia, F., Ramírez, J., Lassl, A., & Salas-Gonzalez, D. (2011). GMM based SPECT image classification for the diagnosis of Alzheimer’s disease. Applied Soft Computing, 11(2), 2313-2325. https://doi.org/10.1016/j.asoc.2010.08.012
[22] Hanyu, H., Sato, T., Hirao, K., Kanetaka, H., Iwamoto, T., & Koizumi, K. (2010). The progression of cognitive deterioration and regional cerebral blood flow patterns in Alzheimer's disease: A longitudinal SPECT study. Journal of the Neurological Sciences, 290(1-2), 96-101. https://doi.org/10.1016/j.jns.2009.10.022
[23] Graña, M., Termenon, M., Savio, A., Gonzalez-Pinto, A., Echeveste, J., Pérez, J. M., & Besga, A. (2011). Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation. Neuroscience Letters, 502(3), 225-229. https://doi.org/10.1016/j.neulet.2011.07.049
[24] Lee, W., Park, B., & Han, K. (2013). Classification of diffusion tensor images for the early detection of Alzheimer's disease. Computers in Biology and Medicine, 43(10), 1313-1320. https://doi.org/10.1016/j.compbiomed.2013.07.004
[25] Oktavian, M. W., Yudistira, N., & Ridok, A. (2022). Classification of Alzheimer's disease using the Convolutional Neural Network (CNN) with transfer learning and weighted loss. International Association of Engineers Journal of Computer Science, 50(3), 947-953. https://doi.org/10.48550/arXiv.2207.01584
[26] Liu, Y., Tang, K., Cai, W., Chen, A., Zhou, G., Li, L., & Liu, R. (2022). MPC-STANet: Alzheimer’s Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism. Frontiers in Aging Neuroscience, 14. https://doi.org/10.3389/fnagi.2022.918462
[27] Shu, T., Zhang, B., & Tang, Y. Y. (2020). Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced Classification. Institute of Electrical and Electronics Engineers Transactions on Neural Networks and Learning Systems, 31(8), 2847-2856. https://doi.org/10.1109/TNNLS.2018.2884444
[28] Li, Z., Kamnitsas, K., & Glocker, B. (2021). Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation. Institute of Electrical and Electronics Engineers Transactions on Medical Imaging, 40(3), 1065-1077. https://doi.org/10.1 109/TMI.2020.3046692
[29] Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. Institute of Electrical and Electronics Engineers Transactions on Neural Networks and Learning Systems, 33(12), 6999-7019. https://doi.org/10.1109/TNNLS.2021.3084827
[30] Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. https://doi.org/10.1016/j.aci.2018.08.003
[31] Mandrekar, J. N. (2010). Receiver Operating Characteristic Curve in Diagnostic Test Assessment. Journal of Thoracic Oncology, 5(9), 1315-1316. https://doi.org/10.109 7/JTO.0b013e3181ec173d
[32] Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment. Institute of Electrical and Electronics Engineers Access, 9, 78368-78381. https://doi.org/10.1109/ACCESS.2021.3084050
[33] Sharma, S., Gupta, S., Gupta, D., Altameem, A., Saudagar, A. K. J., Poonia, R. C., & Nayak, S. R. (2022). HTLML: Hybrid AI Based Model for Detection of Alzheimer’s Disease. Diagnostics, 12(8), 1833. https://doi.org/10.3390/diagnostics12081833
[34] El-Latif, A. A. A., Chelloug, S. A., Alabdulhafith, M., & Hammad, M. (2023). Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data. Diagnostics, 13(7), 1216. https://doi.org/10.3390/diagnostics13071216
[35] Murugan, S., Venkatesan, C., Sumithra, M. G., Gao, X. Z., Elakkiya, B., Akila, M., & Manoharan, S. (2021). DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images. Institute of Electrical and Electronics Engineers Access, 9, 90319-90329. https://doi.org/10.1109/ACCESS.2 021.3090474
[36] Al-Adhaileh, M. H. (2022). Diagnosis and classification of Alzheimer's disease by using a convolution neural network algorithm. Soft Computing, 26(16), 7751-7762. https:/ /doi.org/10.1007/s00500-022-06762-0
[37] Kaur, S., Gupta, S., Singh, S., & Gupta, I. (2022). Detection of Alzheimer’s Disease Using Deep Convolutional Neural Network. International Journal of Image and Graphics, 22(03), 2140012. https://doi.org/10.1142/s021946782140012x
[38] Sisodia, P. S., Ameta, G. K., Kumar, Y., & Chaplot, N. (2023). A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images. Archives of Computational Methods in Engineering, 30(4), 2409-2429. http s://doi.org/10.1007/s11831-022-09870-0
[39] Shukla, A., Tiwari, R., & Tiwari, S. (2023). Alz-ConvNets for Classification of Alzheimer Disease Using Transfer Learning Approach. Springer Nature Computer Science, 4(4), 404. https://doi.org/10.1007/s42979-023-01853-7
[40] Sharma, S., Guleria, K., Tiwari, S., & Kumar, S. (2022). A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans. Measurement: Sensors, 24, 100506. https://doi.org/10.10 16/j.measen.2022.100506
[41] Fareed, M. M. S., Zikria, S., Ahmed, G., Mui Zzud, D., Mahmood, S., Aslam, M., Jillani, S. F., Moustafa, A., & Asad, M. (2022). ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans. Institute of Electrical and Electronics Engineers Access, 10, 96930-96951. https://doi.org/10.1109/ACCESS. 2022.3204395
[42] Assmi, A., Elhabyb, K., Benba, A., & Jilbab, A. (2024). Alzheimer’s disease classification: a comprehensive study. Multimedia Tools and Applications, 83(27), 70193-70216. https://doi.org/10.1007/s11042-024-18306-9
دوره 21، شماره 3
فنی و مهندسی
پاییز 1403
صفحه 277-299

  • تاریخ دریافت 16 تیر 1403
  • تاریخ بازنگری 23 خرداد 1403
  • تاریخ پذیرش 24 مرداد 1403