Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Stadium of breast cancer can be detected by using mammographic images. The accuracy is strongly influenced by the image quality. In this study, we propose a point operation of intensity adjustment to enhance the quality of the images. We implement the Fuzzy Neural Network (FNN) Model for breast cancer classification based on the enhaced mammographic images. Then, the images are extracted by using Gray Level Co-occurrence Matrix (GLCM) method to obtain the parameter values of the images. The fuzzification of the parameter values is required to generate the inputs of the FNN Model which are in the form of fuzzy numbers instead of classic numbers. We compare the performances of the FNN models with and without the point operation. The results demonstrate that on the training data both FNN models deliver satisfied performance with no misclassified data. While on the testing data, the FNN Model with point operation outperforms the FNN model without point operation. This result suggests a strong effectiveness of the mammographic images preprocessing point operation to increase the accuracy of the FNN Model to classify breast cancer.
How to cite this article:
Dhoriva Urwatul Wutsqa and Rahmada Putri Setiadi. Point Operation to Enhance the Performance of Fuzzy Neural Network
Model for Breast Cancer Classification.
DOI: https://doi.org/10.36478/jeasci.2017.4405.4410
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2017.4405.4410