Computer Aided Diagnosis of Mammography Cancer

Abstract

In this study, computer-aided detection (CADe) system is optimized to reduce radiologists’ workload and to improve accuracy of cancer detection by providing more quantitative (objective) decisions added to the qualitative (subjective) assessment of radiologists. The images have been collected from MIAS database. 3 databases were prepared by 3 different ROIs sizes (32x32, 42x42 & 52x52 pixels). Then, prepressing is done to enhance the peripheral of ROIs. This CADe computed parametric features from ROIs using statistics, histogram, GLCM and wavelet techniques. Sequential Forward Selection (SFS) technique is used to study the significance of features and eventually to omit redundancies. Several types of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were trained to differentiate between normal and abnormal ROIs, then tested on another non-training set. Best overall performance results obtained with ROI size of 32x32 and histogram of 32 levels (Accuracy = 97.37%, Sensitivity= 95%, Specificity = 100%, PPV = 100% and NPV = 94.74). The results also indicate some useful features are well-representing to abnormalities across different classifiers such as: Mean, STD, Square of STD, Mode, Median, Quantile (10%), Quantile (70%), Quantile (90%), Percentile (30%), throughout multiple histogram levels both in spatial and DWT spaces.