Classification of microcalcification in mammogram images using Enhanced Support Vector Machine (ESVM) / Muhammad Akmal Firdaus... [et al.]

Support Vector Machine (SVM) is a supervised machine learning algorithm with the
ability to build a classification model from a labeled dataset. SVM has been broadly used
in image classification of medical imaging such as mammogram images for breast cancer
detection due to its higher classification precision, higher prediction accuracy, better
generalization capability and better overall performance. Breast cancer detection is
critically dependent on early detection and accurate diagnosis. However, the existed
histopathological classification of breast cancer has clinical utility that is limited due to
insufficient prognostic and predictive power. The diagnostic decisions by experienced
physicians can be increased by an effective medical decision support system. Therefore,
this research concentrates on Enhanced Support Vector Machine (ESVM) that combines
Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) for better
data classification accuracy. DWT is used for the extraction of statistical and textures
features of mammogram image. Meanwhile, the employment of PCA is to reduce the
dimensionality of the datasets to avoid overfitting thus better accuracy can be achieved.
The ESVM focuses on classification of microcalcification in 30 mammogram images
obtained from the National Cancer Society Malaysia (NCSM). The performance of the
ESVM method is measured in term of classification rate (CR), specificity (SP), sensitivity
(SY) and the accuracy based on Receiving Operating Characteristics (ROC) curve
obtained by comparing the images classification results of ESVM and expert findings. The
result revealed that the classification rate is 90% while for the accuracy based on the ROC
Curve is 0.9375. Based on the result obtained, it is proven that ESVM is excellent in
classifying the microcalcification in mammogram images and act as an assistance for
radiologists that provides "second judgment" on medical image readings.