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Interpretable Optimization Training Strategy-Based DCNN and Its Application on CT Image Recognition

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成果类型:
期刊论文
作者:
Wang, Ronghan;Liu, Tao;Lu, Junwei;Zhou, Yuwei
通讯作者:
Wang, RH
作者机构:
[Wang, Ronghan; Wang, RH] Hunan Inst Sci & Tech Informat, Changsha 410001, Hunan, Peoples R China.
[Liu, Tao] Second Peoples Hosp Hunan Prov, Changsha 410021, Hunan, Peoples R China.
[Lu, Junwei] Hunan Agr Univ, Oriental Sci & Technol Coll, Changsha 410128, Hunan, Peoples R China.
[Zhou, Yuwei] Changsha Hosp Tradit Chinese Med, Changsha 410100, Hunan, Peoples R China.
通讯机构:
[Wang, RH ] H
Hunan Inst Sci & Tech Informat, Changsha 410001, Hunan, Peoples R China.
语种:
英文
期刊:
Mathematical Problems in Engineering
ISSN:
1024-123X
年:
2022
卷:
2022
基金类别:
Research and Development Project of Hunan Institute of Science and Technology Information, China [2018305]
机构署名:
本校为其他机构
院系归属:
东方科技学院
摘要:
The theoretical basis of the discrete random sample batch classification is not clear and the sample division is not scientific during the process of Deep Convolutional Neural Network (DCNN) model training. Aiming at the problems above, starting from the DCNN detection recognition mechanism, the theory of random discrete samples is given and proved, and a scientific quantitative batch of sample input method is proposed. Combined with image preprocessing, based on the strategy of random dispersion of samples, and scientifically quantified sample...

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