Deep Learning Algorithms Based on Computer Vision Technology and Large-Scale Image Data

Jingxiao Tian; Yaqian Qi; Hanzhe Li; Yuan Feng; Xiangxiang Wang

doi:10.5281/zenodo.11072457

Authors

Jingxiao Tian San Diego State University
Yaqian Qi Baruch College
Hanzhe Li New York University
Yuan Feng Duke University
Xiangxiang Wang University of Texas at Arlington

DOI:

https://doi.org/10.5281/zenodo.11072457

References:

29

Keywords:

Computer Vision, Deep Learning, Image Processing, Medical Imaging

Abstract

This paper explores the profound impact of deep learning technology on computer vision and its wide-ranging applications across multiple domains. From pedestrian detection to medical image analysis, deep learning's versatility is harnessed alongside traditional methods, ushering in a new era of efficiency and accuracy. Large-scale image storage solutions, coupled with advanced retrieval and processing techniques, ensure the seamless handling of vast datasets. In the medical realm, imaging technologies have undergone significant transformation, with medical image processing emerging as a crucial component in diagnostics and treatment planning. Leveraging innovative hardware and iterative algorithms, this field continues to evolve, promising enhanced capabilities in non-invasive diagnostics and personalized medicine.

Author Biographies

Jingxiao Tian, San Diego State University

Electrical and Computer Engineering, San Diego State University, CA, USA.

Yaqian Qi, Baruch College

Quantitative Methods and Modeling, Baruch College, NY, USA.

Hanzhe Li, New York University

Computer Engineering, New York University, NY, USA.

Yuan Feng, Duke University

Interdisciplinary Data Science , Duke University , North Carolina, USA.

Xiangxiang Wang, University of Texas at Arlington

Computer Science, University of Texas at Arlington, Arlington, TX, USA.

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