Mask Region-based Convolution Neural Network (Mask R-CNN) Classification of Alzheimer’s Disease Based on Magnetic Resonance Imaging (MRI)

Mask Region-based Convolution Neural Network (Mask R-CNN) Classification of Alzheimer’s Disease Based on Magnetic Resonance Imaging (MRI)

Автор: Anil Kumar Pallikonda, P. Suresh Varma, B. Vivekanandam

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 6 vol.15, 2023 года.

Бесплатный доступ

Alzheimer's disease is a progressive neurologic disorder that causes the brain to shrink (atrophy) and brain cells to die. A recent study found that 40 million people worldwide suffer from Alzheimer's disease (AD). A few symptoms of this AD disease are problems with language understanding, mood swings, behavioral issues, and short-term memory loss. A key research area for AD is the classification of stages. In this paper, we applied both binary and multi-class classification. In this paper, proposed is a Mask-Region based Convolution Neural Network (R-CNN) for classifying the stages including MCI, LMCI, EMCI, AD, and CN of Alzheimer's Disease. First performing pre-processing by using the skull-stripping algorithm for removing the noise. Second, the patch wise U-Net has been employed to segment the images for improving the classification process. After that, the system's efficiency is examined using MATLAB-based experiments, utilizing images from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to evaluate the efficiency in terms of accuracy, precision, recall, specificity, and sensitivity. Our proposed approach to classifying the stages achieves about 98.54%,94.2%, 98.25%, 99.2%, and 99.02%in terms of accuracy with EMCI, CN, MCI, AD, and LMCI respectively. Proposing mask R-CNN with segmentation to classify from CN to AD subjects successfully improved classifier accuracy significantly on the ADNI datasets.

Еще

Alzheimer's disease (ad), multiple stages, principal component analysis, a neurodegenerative disorder, and feature dimension

Короткий адрес: https://sciup.org/15018847

IDR: 15018847   |   DOI: 10.5815/ijigsp.2023.06.05

Список литературы Mask Region-based Convolution Neural Network (Mask R-CNN) Classification of Alzheimer’s Disease Based on Magnetic Resonance Imaging (MRI)

  • Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., &Mehmood, Z. (2020). 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.
  • Raju, M., Gopi, V. P., Anitha, V. S., & Wahid, K. A. (2020). Multi-class diagnosis of Alzheimer’s disease using cascaded three dimensional-convolutional neural network. Physical and Engineering Sciences in Medicine, 43(4), 1219-1228.
  • Basheera, S., & Ram, M. S. S. (2019). Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 5, 974-986.
  • Basheera, S., & Ram, M. S. S. (2020). A novel CNN based Alzheimer’s disease classification using hybrid enhanced ICA segmented gray matter of MRI. Computerized Medical Imaging and Graphics, 81, 101713.
  • El-Sappagh, S., Abuhmed, T., Islam, S. R., &Kwak, K. S. (2020). Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data. Neurocomputing, 412, 197-215.
  • Eroglu, Y., Yildirim, M. and Cinar, A., (2022). MRMR‐based hybrid convolutional neural network model for classification of Alzheimer's disease on brain magnetic resonance images. International Journal of Imaging Systems and Technology, 32(2), pp.517-527.
  • Jiang, X., Chang, L., & Zhang, Y. D. (2020). Classification of Alzheimer’s disease via eight-layer convolutional neural network with batch normalization and dropout techniques. Journal of Medical Imaging and Health Informatics, 10(5), 1040-1048.
  • Salehi, A. W., Baglat, P., & Gupta, G. (2020). Alzheimer’s disease diagnosis using deep learning techniques. Int. J. Eng. Adv. Technol, 9(3), 874-880.
  • Kumar, P. R., Arunprasath, T., Rajasekaran, M. P., &Vishnuvarthanan, G. (2018). Computer-aided automated discrimination of Alzheimer's disease and its clinical progression in magnetic resonance images using hybrid clustering and game theory-based classification strategies. Computers & Electrical Engineering, 72, 283-295.
  • Maqsood, M., Nazir, F., Khan, U., Aadil, F., Jamal, H., Mehmood, I., & Song, O. Y. (2019). Transfer learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI scans. Sensors, 19(11), 2645.
  • Surendran, N. and Ahammed Muneer, K.V., (2017). Multistage Classification of Alzheimer ’s disease. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), 6.
  • Fulton, L. V., Dolezel, D., Harrop, J., Yan, Y., & Fulton, C. P. (2019). Classification of Alzheimer’s disease with and without imagery using gradient boosted machines and ResNet-50. Brain sciences, 9(9), 212.
  • Yao, D., Calhoun, V. D., Fu, Z., Du, Y., & Sui, J. (2018). An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment. Journal of neuroscience methods, 302, 75-81.
  • Cui, X., Xiang, J., Guo, H., Yin, G., Zhang, H., Lan, F., & Chen, J. (2018). Classification of Alzheimer's disease, mild cognitive impairment, and normal controls with subnetwork selection and graph Kernel principal component analysis based on minimum spanning tree brain functional network. Frontiers in computational neuroscience, 12, 31.
  • Jo, T., Nho, K., &Saykin, A. J. (2019). Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data. Frontiers in aging neuroscience, 11, 220.
  • An, N., Ding, H., Yang, J., Au, R., &Ang, T. F. (2020). Deep ensemble learning for Alzheimer's disease classification. Journal of biomedical informatics, 105, 103411.
  • Bi, X. A., Cai, R., Wang, Y., & Liu, Y. (2019). Effective diagnosis of alzheimer’s disease via multimodal fusion analysis framework. Frontiers in genetics, 10, 976.
  • Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X., & Zhao, X. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing, 333, 145-156.
  • Feng, C., Elazab, A., Yang, P., Wang, T., Zhou, F., Hu, H., ...& Lei, B. (2019). Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 7, 63605-63618.
  • Jain, R., Jain, N., Aggarwal, A., &Hemanth, D. J. (2019). Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research, 57, 147-159.
  • Mehmood, A., Maqsood, M., Bashir, M., &Shuyuan, Y. (2020). A deep siamese convolution neural network for multi-class classification of alzheimer disease. Brain sciences, 10(2), 84.
  • Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Shen, L., ...& Alzheimer’s Disease Neuroimaging Initiative. (2020). A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage, 208, 116459.
  • Ajagbe, S. A., Amuda, K. A., Oladipupo, M. A., AFE, O. F., &Okesola, K. I. (2021). Multi-classification of alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches. International Journal of Advanced Computer Research, 11, 53.
  • Suh, C. H., Shim, W. H., Kim, S. J., Roh, J. H., Lee, J. H., Kim, M. J., ... & Alzheimer’s Disease Neuroimaging Initiative. (2020). Development and Validation of a Deep Learning–Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. American Journal of Neuroradiology, 41(12), 2227-2234.
  • Tuan, T. A., Pham, T. B., Kim, J. Y., & Tavares, J. M. R. (2020). Alzheimer’s diagnosis using deep learning in segmenting and classifying 3D brain MR images. International Journal of Neuroscience, 1-10.
  • Yildirim, M., &Cinar, A. C. (2020). Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method. Ingénierie des Systèmes d Inf., 25(4), 413-418.
  • Zhang, F., Li, Z., Zhang, B., Du, H., Wang, B., & Zhang, X. (2019). Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease. Neurocomputing, 361, 185-195.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp