Computer Vision in Healthcare: Applications and Challenges in Medical Imaging Analysis
Keywords:
Computer Vision, Medical Imaging, Convolutional Neural Networks, Diagnostic Imaging, Healthcare Technology, Image Segmentation, Machine Learning in HealthcareAbstract
Computer Vision (CV) has significantly transformed healthcare, particularly in the realm of medical imaging, where it plays a crucial role in enhancing diagnostic accuracy, treatment planning, and patient outcomes. Leveraging advanced algorithms such as convolutional neural networks (CNNs), CV applications have been developed to identify, segment, and analyze intricate patterns within medical images, such as X-rays, CT scans, MRI, and histopathological images. However, challenges remain, including issues related to data quality, model interpretability, computational complexity, and regulatory compliance, which impact the widespread adoption of CV solutions in clinical practice. This paper discusses the applications of computer vision in medical imaging analysis, examines key technical and ethical challenges, and highlights emerging trends aimed at overcoming these barriers to enable more robust and reliable healthcare solutions.
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Copyright (c) 2020 KANDIMALLA RENUKA (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
