Arseam

International Journal of Advances in Engineering & Scientific Research

LIGHTWEIGHT REG NET-DRIVEN DEEP LEARNING FRAMEWORK FOR ENHANCED CLASSIFICATION OF NEURODEGENERATIVE DISEASES FROM MRI IMAGES

Sai Suman Singamsetty

Sai suman Singamsetty, Senior Consultant, San Antonio, TX-78259, USA

DOI : https://doi.org/10.5281/zenodo.15034011 Page No : 28-37

Published Online : 2023-06-30

Download Full Article : PDF Check for Updates

ABSTRACT

Neurodegenerative diseases significantly impact global health, necessitating early and accurate diagnosis. Current diagnostic methods combining MRI imaging and traditional machine learning algorithms often face accuracy limitations and computational inefficiencies. This research presents an efficient deep learning framework that employs an adaptive cross-guided bilateral filter for artifact removal, a lightweight Regularized Network (RegNet) with depth wise separable convolutions for multi-scale feature extraction, and ensemble machine learning techniques for accurate classification into Alzheimer's disease (AD), Parkinson's disease (PD), or Normal categories. This approach maintains high accuracy while substantially reducing computational demands. The proposed framework's efficacy is evaluated using precision, recall, F1-score, Area under the Curve (AUC), and accuracy metrics, demonstrating significant practical potential in clinical settings.

Keywords: Neurodegenerative diseases, MRI, Deep learning, RegNet, Ensemble classification, Depth wise separable convolution, Artifact removal, Computational efficiency.

REFERENCES

Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, (2015). Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge. NeuroImage, 111, 562-579.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1251-1258.
Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., ... & Jack, C. R. (2016). Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimer's & Dementia, 12(3), 292-323.
Filippi, M., Elisabetta, S., Piramide, N., & Agosta, F. (2018). Functional MRI in idiopathic Parkinson's disease. International Review of Neurobiology, 141, 439-467.
Iglesias, J. E., Augustinack, J. C., Nguyen, K., Player, C. M., Player, A., Wright, M., ... & Fischl, B. (2015). A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage, 115, 117-137.
Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., ... & Frackowiak, R. S. (2015). Automatic classification of MR scans in Alzheimer's disease. Brain, 131(3), 681-689.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Wolpert, D. H. (2019). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341-1390.
World Health Organization. (2021). Global status report on the public health response to dementia. World Health Organization.
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., ... & Silverberg, N. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimer's & Dementia, 14(4), 535-562.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.
Vieira, S., Pinaya, W. H., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58-75.
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2019). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55(3), 856-867.
Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016). Alzheimer's disease diagnostics by adaptation of 3D convolutional network. IEEE International Conference on Image Processing (ICIP), 126-130.
Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61-78.
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774-781.
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240-1251.

Singamsetty S.S (2023), “Lightweight Reg Net-Driven Deep Learning Framework for Enhanced Classification of Neurodegenerative Diseases from MRI Images”, International Journal of Advances in Engineering & Scientific Research, Volume 10, Issue 1, 2023, pp 28-37

DOI : https://doi.org/10.5281/zenodo.15034011

Article View: 29
PDF Download: 1

REFERENCES

Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, (2015). Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge. NeuroImage, 111, 562-579.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1251-1258.
Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., ... & Jack, C. R. (2016). Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimer's & Dementia, 12(3), 292-323.
Filippi, M., Elisabetta, S., Piramide, N., & Agosta, F. (2018). Functional MRI in idiopathic Parkinson's disease. International Review of Neurobiology, 141, 439-467.
Iglesias, J. E., Augustinack, J. C., Nguyen, K., Player, C. M., Player, A., Wright, M., ... & Fischl, B. (2015). A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage, 115, 117-137.
Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., ... & Frackowiak, R. S. (2015). Automatic classification of MR scans in Alzheimer's disease. Brain, 131(3), 681-689.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Wolpert, D. H. (2019). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341-1390.
World Health Organization. (2021). Global status report on the public health response to dementia. World Health Organization.
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., ... & Silverberg, N. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimer's & Dementia, 14(4), 535-562.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.
Vieira, S., Pinaya, W. H., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58-75.
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2019). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55(3), 856-867.
Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016). Alzheimer's disease diagnostics by adaptation of 3D convolutional network. IEEE International Conference on Image Processing (ICIP), 126-130.
Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61-78.
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774-781.
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240-1251.

Submission Instruction

Call for Paper

Submit Paper Online

Join Editorial Board

Author Guidelines

Impact Factor: 6.013

Indexed in

LIGHTWEIGHT REG NET-DRIVEN DEEP LEARNING FRAMEWORK FOR ENHANCED CLASSIFICATION OF NEURODEGENERATIVE DISEASES FROM MRI IMAGES

Aims and Objectives

Ethics Policy

Peer Review Policy

Call for Paper

Conference

Instruction To Review

Guide To Authors