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- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 8, Special Issue 1 (2019)
The role of convolutional neural networks in automated diagnosis of neurological disorders: A critical analysis
Author(s):
Dr. Yogesh Bhomia, Dr. Sunil Kumar Mishra and Rajesh Pal
Dr. Yogesh Bhomia, Dr. Sunil Kumar Mishra and Rajesh Pal
Abstract:
In recent years, the application of Convolutional Neural Networks (CNNs) in the realm of medical diagnostics has gained significant attention, particularly in the context of automated diagnosis of neurological disorders. This review paper critically examines the evolving role of CNNs in revolutionizing the diagnostic landscape for neurological conditions. The burgeoning complexity and prevalence of neurological disorders necessitate innovative approaches to diagnosis, and CNNs have emerged as promising tools in this pursuit.
In recent years, the application of Convolutional Neural Networks (CNNs) in the realm of medical diagnostics has gained significant attention, particularly in the context of automated diagnosis of neurological disorders. This review paper critically examines the evolving role of CNNs in revolutionizing the diagnostic landscape for neurological conditions. The burgeoning complexity and prevalence of neurological disorders necessitate innovative approaches to diagnosis, and CNNs have emerged as promising tools in this pursuit.
The paper begins by providing an overview of the current state of neurological disorder diagnosis and the challenges faced by traditional methods. The limitations of manual interpretation, time-consuming processes, and the subjectivity inherent in human analysis underscore the need for automated systems that can enhance accuracy, efficiency, and objectivity. CNNs, with their ability to automatically learn hierarchical representations from medical imaging data, present a compelling solution to these challenges.
A comprehensive exploration of the architecture and functioning of CNNs in the context of neurological disorder diagnosis is presented. The review elucidates how CNNs excel in feature extraction and pattern recognition from diverse imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). The paper also discusses the importance of large and well-curated datasets in training CNN models for optimal performance.
The critical analysis section evaluates the strengths and limitations of CNNs in automated diagnosis, considering factors such as interpretability, generalizability across diverse patient populations, and robustness to variations in imaging quality. Furthermore, ethical considerations, including patient privacy and the potential for algorithmic biases, are scrutinized to ensure the responsible deployment of CNNs in clinical settings.
The review concludes by highlighting the future directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between medical professionals, computer scientists, and ethicists. As the integration of CNNs in neurological disorder diagnosis continues to evolve, this critical analysis serves as a valuable resource for researchers, clinicians, and policymakers seeking to navigate the transformative landscape of automated medical diagnostics.
Pages: 01-04 | 170 Views 93 Downloads
How to cite this article:
Dr. Yogesh Bhomia, Dr. Sunil Kumar Mishra and Rajesh Pal. The role of convolutional neural networks in automated diagnosis of neurological disorders: A critical analysis. The Pharma Innovation Journal. 2019; 8(1S): 01-04. DOI: 10.22271/tpi.2019.v8.i1Sa.25233
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
