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Cervical cancer classification using convolutional neural networks and extreme learning machines - 2020

Research Area:  Machine Learning

Abstract:

Cervical cancer is one of the main reasons of death from cancer in women. The complication of this cancer can be limited if it is diagnosed and treated at an early stage. In this paper, we propose a cervical cancer cell detection and classification system based on convolutional neural networks (CNNs). The cell images are fed into a CNNs model to extract deep-learned features. Then, an extreme learning machine (ELM)-based classifier classifies the input images. CNNs model is used via transfer learning and fine tuning. Alternatives to the ELM, multi-layer perceptron (MLP) and autoencoder (AE)-based classifiers are also investigated. Experiments are performed using the Herlev database. The proposed CNN-ELM-based system achieved 99.5% accuracy in the detection problem (2-class) and 91.2% in the classification problem (7-class).

Author(s) Name:  Ahmed Ghoneim,Ghulam Muhammad,Ghulam Muhammad

Journal name:  Future Generation Computer Systems

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.future.2019.09.015

Volume Information:  Volume 102, January 2020, Pages 643-649