DEVELOPMENT OF INTELLIGENT ALGORITHMS FOR MEDICAL IMAGE ANALYSIS IN THE DIAGNOSIS OF NEURODEGENERATIVE DISEASES
DOI:
https://doi.org/10.55640/Keywords:
Neurodegenerative diseases, medical image analysis, deep learning, MRI, artificial intelligence.Abstract
Neurodegenerative diseases represent a major challenge in modern healthcare due to their progressive nature and difficulty in early diagnosis. This study investigates the application of artificial intelligence techniques for analyzing medical imaging data to detect neurodegenerative disorders. In particular, deep learning algorithms such as Convolutional Neural Network are applied to analyze Magnetic Resonance Imaging (MRI) data for identifying structural changes in the brain. The proposed approach integrates medical image preprocessing, feature extraction, and machine learning classification. Experimental results demonstrate that AI-based diagnostic models significantly improve accuracy in detecting diseases such as Alzheimer’s disease and Parkinson’s disease. The proposed method can assist clinicians in early diagnosis and improve decision-making in neurological healthcare.
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1.Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
2.Christopher M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006.
3.Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning. New York: Springer, 2009.
4.S. Kevin Zhou, Hayit Greenspan, and Dinggang Shen, Deep Learning for Medical Image Analysis. Academic Press, 2017.
5.Daniel S. Marcus et al., “Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young and Old Adults,” Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498–1507, 2007.
6.Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, 2012.
7.Oskar Hansson et al., “Machine Learning Approaches for Detecting Alzheimer’s Disease Using MRI Data,” NeuroImage, vol. 183, pp. 464–474, 2018.
8.Sebastian Raschka and Vahid Mirjalili, Python Machine Learning. Birmingham, UK: Packt Publishing, 2019.
9.Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. O’Reilly Media, 2019.
10.World Health Organization, Neurological Disorders: Public Health Challenges. Geneva: WHO Press, 2006.
11.Alzheimer's Association, Alzheimer’s Disease Facts and Figures, Annual Report.
12.IEEE, IEEE Transactions on Medical Imaging, IEEE Journal.
13.Springer, Medical Image Computing and Computer-Assisted Intervention (MICCAI) Conference Proceedings.
14.Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
15.Elsevier, Artificial Intelligence in Medicine Journal, Elsevier Publications.
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