Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer
Diyar Qader Zeebaree, Habibollah Haron, Adnan Mohsin Abdulazeez, Dilovan Asaad Zebari · 2019
In developing countries breast cancer has been found to be one of the diseases that threatens the lives of women, and that is why finding ways of detecting efficiently is of great importance. The detection of breast cancer at an early stage through self-examination is very difficult. In this study, we proposed a new descriptor that can help to identify the abnormality of the breast by enhancing the features of LBP texture and enhance the LPB descriptor by using a new threshold that can help to identify the important information for the detection of abnormal cases. In the next stage, the significant features are extracted from the breast tumours images that have been segmented. Such features could be found in frequency or spatial domain. The extracted features for diagnosing tumour automatically, are additional and different from those features which the radiologist extracts manually. The proposed method demonstrates the possibility of using the LBP based texture feature with the new proposed method for categorising ultrasound images, which registered a high accuracy of 96%, the sensitivity of 94%, specificity of 97%.