Computer Vision Applied in Medical Technology: The Comparison of Image Classification and Object Detection on Medical Images

DOI

10.2991/cecs-18.2018.17How to use a DOI?

Keywords

Deep learning, Convolutional Neuron Network, R-CNN, Medical Technology, Architecture, Comparison.

Abstract

Image classification and object detection are two computer vision techniques that are currently commonly used. In this paper, convolutional neural network (CNN) and region-based CNN (RCNN) are used as examples to analyze and compare image classification and object detection. This paper will analyze the architectural characteristics and application scenarios of these two algorithms and analyzes the different characteristics of these two technologies in medical technology applications. CNN is an infrastructure classification algorithm, and image classification tasks are more common in medical image processing. RCNN is the development of CNN. Object detection technology can directly detect the presence and location of the lesion in medical images with RCNN. Combining the algorithms of the two techniques can also achieve some more complex image processing goals.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haotian Yan
PY  - 2018/07
DA  - 2018/07
TI  - Computer Vision Applied in Medical Technology: The Comparison of Image Classification and Object Detection on Medical Images
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 88
EP  - 93
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.17
DO  - 10.2991/cecs-18.2018.17
ID  - Yan2018/07
ER  -