Medical image analysis using wavelet transform and deep belief networks

作者：

Highlights：

• Propose a robust DBN-based classification model for X-ray images.

• The wavelet transforms improves the deep classification performance.

• The Kolmogorov Smirnov test is applied to find the most discriminative features.

• An appropriately designed DBN selects features resulting in a fast classification.

摘要

•Propose a robust DBN-based classification model for X-ray images.•The wavelet transforms improves the deep classification performance.•The Kolmogorov Smirnov test is applied to find the most discriminative features.•An appropriately designed DBN selects features resulting in a fast classification.

论文关键词：Deep belief network,Wavelet transform,Radiography image,Feature extraction,Kolmogorov Smirnov test,Classification

论文评审过程：Received 20 July 2016, Revised 12 May 2017, Accepted 28 May 2017, Available online 1 June 2017, Version of Record 30 June 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.073