Abstract
This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region ( F G D r o i ) instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi-orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, the Support Vector Machine classifier is used to perform the classification, and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to 86.13 % and 82.02 % accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state-of-the-art in the literature.
Highlights
In 2014, there were more than 55,000 malignant breast cancer cases diagnosed in the UnitedKingdom (UK), with more than 11,000 mortalities [1]
Based on the results reported in the literature, the majority of the proposed methods has used traditional machine learning algorithms tested on 4-class breast density classification and achieved below 80% accuracy
To summarise the performance of the proposed method using different neighbourhood topologies, we present the results evaluated based on the Mammographic Image Analysis Society (MIAS) dataset [18], all in a single graph as shown in medium failed to achieved accuracy above 80%
Summary
In 2014, there were more than 55,000 malignant breast cancer cases diagnosed in the UnitedKingdom (UK), with more than 11,000 mortalities [1]. In 2014, there were more than 55,000 malignant breast cancer cases diagnosed in the United. In the United States (US), it was estimated that more than 246,000 malignant breast cancer cases were diagnosed in 2016, with approximately. Many studies have indicated that breast density is a strong risk factor for developing breast cancer [3,4,5,6,7,8,9,10,11,12] because breast cancer has a very similar appearance to dense tissues, which makes it difficult to detect in mammograms. Their study found that there is a significant association between breast cancer and the Gail risk factor plus Body Mass Index (BMI). An accurate breast density estimation is an important step during the screening procedure because women with dense breasts can be six times more likely to develop breast cancer [1,2]
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