Detection of lung cancer using radiograph images enhancement and radial basis function classifier

Abadi, Agus Maman; Wutsqa, Dhoriva Urwatul; Pamungkas, Leonardus Ragil

doi:10.1109/cisp-bmei.2017.8302052

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…The histogram equalization yields the higher contrast and brighter image, and histogram with intensity level uniformly distributed [28]. Abadi et al [29] have demonstrated the effectiveness of the HFEF and histogram equalization to improve the quality of an image and increase the accuracy of the RBFNN model to detect lung cancer.…”

Section: Image Preprocessingmentioning

confidence: 99%

Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

2021

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In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level cooccurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis.

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Section: Image Preprocessingmentioning

confidence: 99%