Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

D.U. Wutsqa; M. Marwah

doi:10.1088/1742-6596/855/1/012062

← Back to search

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

Open

D.U. Wutsqa, M. Marwah

2017 Journal of Physics: Conference Series Vol. 855 Issue 1 Conference paper Cited by 6 SDG 3

Abstract

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification. © Published under licence by IOP Publishing Ltd.

Affiliations

Mathematics Department, Yogyakarta State University, Yogyakarta, Indonesia

View full text (DOI) →

Research at a Glance

Research at a Glance

Topics & SDG Alignment

Collaboration

Author Profile (Selected)

References Overview

Journal & Source

Metadata & Integrity