Breast cancer classification with wavelet neural network

Ucar, Kursad; Koçer, Hasan Erdinç

doi:10.1109/idap.2017.8090347

Cited by 1 publication

(2 citation statements)

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“…Many researchers worked on a multiresolution analysis of mammograms based on wavelets transform by using different types of wavelet family and feature spaces. Ucar and Kocer (2017) used Wavelet Neural Network (WNN) and classical Neural Network in normal and abnormal classification. The best estimated result of WNN is 98.90%.…”

Section: Related Workmentioning

confidence: 99%

“…Ergin et al (2016) obtained an accuracy value of 72.39% using 322 database FLDA classifier. In addition, Ucar and Kocer (2017) used Wavelet for feature extraction method and ANN classifier obtained an accuracy value of 95.49%. Putra (2018) using 2D DWT+ LBP obtained accuracy values of 92.1% with sensitivity value (Sn) of 91% and Specificity value of (Sp) 94%.…”

Section: Comparison Of Normal and Abnormal Classifications Performancementioning

confidence: 99%

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Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

Wisudawati¹,

Madenda²,

Wibowo³

et al. 2020

MAS

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Breast cancer is one of the leading causes of death worldwide among women. According to GLOBOCAN Data, the International Agency for Research on Cancer (IARC), in 2012 there were 14.067.894 new cases of cancer and 8.201.575 deaths from cancer worldwide (Kementerian Kesehatan Republik Indonesia [KemenkesRI], 2015). Mammography is the most common and effective technique for detecting breast tumors. However, mammograms have poor image quality with low contrast. A Computer-Aided Detection (CAD) system has been developed to help radiologists effectively detect lesions on mammograms that indicate the presence of breast tumor. The feature extraction method in the CAD system is an important part of getting high accuracy results in classifying normal and abnormal breast tumors. By using the combination of 2D-Discrete wavelet transform and Gray-Level Co-Occurrence Matrix (GLCM) obtained an accuracy value of 100% on MIAS and UDIAT Database in classifying the presence of masses in the mammogram image and obtained an accuracy value of 93.8% for classifying normal, benign, and malignant. The proposed method has the potential to identify the presence of masses in the mammogram image as a decision support system to the radiologist.

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Section: Related Workmentioning

confidence: 99%

Section: Comparison Of Normal and Abnormal Classifications Performancementioning

confidence: 99%