Deep Learning Approaches for Automated Diagnosis of Neurological Disorders

Authors

  • Priya Desai Research Scientist, AI Lab, Ganges Institute of Technology, Mumbai, India Author

Keywords:

Deep Learning, Neurological Disorders, Automated Diagnosis, Medical Imaging, Convolutional Neural Networks, Neuroimaging, Healthcare

Abstract

The field of medical diagnostics has seen significant advancements with the integration of deep learning techniques. Neurological disorders, in particular, pose unique challenges due to the complexity and variability of symptoms. This paper explores the application of deep learning approaches for automating the diagnosis of neurological disorders. By leveraging large datasets and sophisticated neural networks, these methods offer the potential for more accurate and timely diagnosis, leading to improved patient outcomes. This paper reviews recent developments in deep learning-based diagnostics for neurological disorders, discusses challenges and future directions, and highlights the impact of these technologies on clinical practice.

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Published

16-04-2024

How to Cite

[1]
Priya Desai, “Deep Learning Approaches for Automated Diagnosis of Neurological Disorders”, J. of Artificial Int. Research and App., vol. 4, no. 1, pp. 22–31, Apr. 2024, Accessed: Nov. 22, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/3

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