Computer-Aided Diagnosis of Mammography Cancer 
Loai Kinani1, Umar Alqasemi2
1Loai Kinani*, Graduate Student of M.Sc. Program of Biomedical Engineering, Dept. of Electrical and Computer Engineering, King Abdulaziz University, Saudi Arabia
2Umar Alqasemi, Associate Professor of Biomedical Engineering, Dept. of Electrical and Computer Engineering, King Abdulaziz University, Saudi Arabia
Manuscript received on May 30, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 725-731 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9805069520/2020©BEIESP | DOI: 10.35940/ijeat.E9805.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 (32×32, 42×42 & 52×52 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 32×32 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.
Keywords: Mammogram, Mass, Microcalcification, computer-aided detection, medical image recognition, support vector machine.