增强图像识别:在专业医疗数据集上利用机器学习

Q2 Computer Science
Nidhi Agarwal, Nitish Kumar, None Anushka, Vrinda Abrol, Yashica Garg
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引用次数: 0

摘要

导读:图像识别在从医疗保健到自动驾驶汽车等众多行业中发挥着关键作用。机器学习技术,特别是深度学习算法,通过使计算机能够以高精度识别和分类图像中的物体,彻底改变了图像识别领域。 目的:本研究论文深入探讨了机器学习算法在图像识别任务中的应用,包括监督学习、卷积神经网络(cnn)和迁移学习。 方法:本文讨论了与图像识别相关的挑战,如数据集大小和质量、过拟合和计算资源。& # x0D;结果:它突出了新兴趋势和未来的研究方向,包括可解释性和可解释性,对抗性攻击和鲁棒性,以及实时和基于边缘的识别。& # x0D;结论:总之,该研究强调了深度学习算法的变革性影响,解决了图像识别方面的挑战。持续关注新兴趋势对于提高各种应用的准确性和效率至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Image Recognition: Leveraging Machine Learning on Specialized Medical Datasets
INTRODUCTION: Image recognition plays a pivotal role in numerous industries, ranging from healthcare to autonomous vehicles. Machine learning techniques, especially deep learning algorithms, have revolutionized the field of image recognition by enabling computers to identify and classify objects within images with high accuracy. OBJECTIVES: This research paper provides an in-depth exploration of the application of machine learning algorithms for image recognition tasks, including supervised learning, convolutional neural networks (CNNs), and transfer learning. METHODS: The paper discusses the challenges associated with image recognition, such as dataset size and quality, overfitting, and computational resources. RESULTS: It highlights emerging trends and future research directions, including explainability and interpretability, adversarial attacks and robustness, and real-time and edge-based recognition. CONCLUSION: In conclusion, the study emphasizes the transformative impact of deep learning algorithms, addressing challenges in image recognition. Ongoing focus on emerging trends is vital for enhancing accuracy and efficiency in diverse applications.
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来源期刊
EAI Endorsed Transactions on Pervasive Health and Technology
EAI Endorsed Transactions on Pervasive Health and Technology Computer Science-Computer Science (miscellaneous)
CiteScore
3.50
自引率
0.00%
发文量
14
审稿时长
10 weeks
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