Transformed image features can improve machine learning performance for detecting benign-malignant of breast cancer
Keywords:
Machine Learning, Breast Cancer, Benign, Malignant, Mammography, transformation data.Abstract
Breast cancer is a commonly diagnosed disease in women. This research aimed to create a transformed image features can improve machine learning performance for detecting benign-malignant of breast cancer.This research was quantitative research. Data was taken from the radiology installation at Doctor Sutomo Hospital from 2010 until now, where there were 670 data, consisting of 342 benign and 328 malignant. The data was distributed randomly; 70% was used for training, while the remaining 30% was used for testing. Every mammography had nine features: Entropy, Entropy of hdiff, contrast, Angular second moment, Angular second moment of hdiff Inverse difference moment, mean, Mean hdiff, and deviation. This research developed each feature into ten sub-features, namely Entropy at a distance of 1 pixel to Entropy at a distance of 10 pixels, and so on until the mean Hdiff at a distance of 10 pixels. Thus, the total features used in this research were 90 features. This research used three types of transformation, namely original, binary transformation, and bipolar transformation. Besides, this research also used three types of methods, namely 90 features, average, and optimization. Furthermore, this research sought the best performance based on the widest ROC graph, highest accuracy, and lowest false negative rate. In addition, this research also sought the best types of transformation and methods. Models with optimization types with binary and bipolar transformations had the highest positive true values. Models with optimization types with binary and bipolar transformations both had the lowest false negative values. The average type model with bipolar transformation had the highest accuracy value, followed by binary transformation. Models with optimization types with binary and bipolar transformations both had the highest ROC area. Based on the three methods and three transformations proposed, it was found that the optimization method and types of binary and bipolar transformations had the best performance.
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