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Recognition and Clinical Diagnosis of Cervical Cancer Cells Based on our Improved Lightweight Deep Network for Pathological Image

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Accurate recognition of cervical cancer cells is of great significance to clinical diagnosis, but these existing algorithms are designed by low-level manual feature, and their performance improvements are limited an improved algorithm based on residual neural network is proposed to improve the accuracy of diagnosis. Firstly, momentum parameters are introduced into the training model; secondly, by changing the number of training samples, the recognition rate of the algorithm can be improved. Therefore, aiming at the task of object recognition under resource constrained condition, we optimize the design method of the network structure such as convolution operation, model parameter compression and enhancement of feature expression depth, and design and implement the lightweight network model structure for embedded platform. Our proposed deep network model can reduce the parameters of the model and the resources needed for operation under the condition of guaranteeing the precision. The experimental results show that the lightweight deep model has better performance than that of the existing comparison models, and it can achieve the model accuracy of 94.1% under the condition that the model with fewer parameters on cervical cells data set.

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Authors and Affiliations

  1. Women’s Hospital School Of Medicine Zhejiang University, Hangzhou, 310006, Zhejiang, China

    Hongzhu Wang, Chuan Jiang & Kunzhong Bao

  2. Faculty of Engineering, University of Yamanashi, Kofu, 4000016, Japan

    Caie Xu

Authors
  1. Hongzhu Wang
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  2. Chuan Jiang
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Correspondence to Caie Xu.

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Wang, H., Jiang, C., Bao, K. et al. Recognition and Clinical Diagnosis of Cervical Cancer Cells Based on our Improved Lightweight Deep Network for Pathological Image. J Med Syst 43, 301 (2019). https://doi.org/10.1007/s10916-019-1426-y

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