## 4 Experimental Results  
### Image Acquisition  
In this study, two datasets are used to compare the proposed approach with existing methods. Using images from the mammogram database obtained from a sensible population is necessary in the evaluation of the mammogram classification performance approaches. The testing of such schemes is carried out based on specific protocols that guide the division of the mammogram dataset into testing and training sets for classification. Several mammogram databases can be used to compare results during experiments. In this study four widely used and publicly available mammogram databases, namely the Mini Mammographic Image Analysis Society (Mini-MIAS) [53], the Digital Database for Mammography Screening (DDSM) [54], INbreast [55] and the Breast Cancer Digital Repository (BCDR) [56], are used. The Mini-MIAS database is made up of 322 mammograms and is freely available to the public for scientific research. The 322 mammogram images were collected from 161 pairs of mediolateral oblique (MLO) views from both right and left views. These mammogram images were obtained from a film screen imaging process conducted by a national breast screening program in the UK. This database contains two major categories, abnormal and normal mammograms. It contains 207 benign and 115 malignant mammograms; the total is equal to 322. However, depending on both MLO and craniocaudal (CC) mammogram views, the DDSM contains 2620 cases. It contains normal, benign, and malignant categories with proven pathological information. The mammogram images in this dataset were obtained from different sources, including the Washington University of St. Louis School of Medicine and the Wake Forest University School of Medicine. Figure 6 shows samples from both databases. Figure 6: Mammogram samples: (**a**) Mini-MIAS database [49]; (**b**) DDSM database [50]. The INbreast dataset has images from 115 patients (cases). From each of 90 patients, 4 breast images of both breasts (right and left) were collected, while 50 breast images were collected from 25 mastectomy patients. Therefore, 410 normal, benign, and malignant cases of mammogram images were collected, including MLO and CC views. The Breast Cancer Digital Repository (BCDR) database contains 1125 images from both views MLO and CC totaling 3703 mammogram images with 720 \(\times\) 1168-pixel resolution. Figure 7 shows samples from both databases. This study takes digitalized mammogram images collected from Mini-MIAS, DDSM, INbreast, and BCDR datasets. A total of 316 and 981 mammogram images are collected from both the Mini-MIAS and DDSM databases, respectively. Mini-MIAS has 206 benign and 110 malignant whereas DDSM has 479 benign and 502 malignant. INbreast has a total of 200 more mammogram images, including 127 benign and 73 malignant. Finally, 736 more images are taken from the BCDR database, which include 426 benign and 310 malignant. As a result, a total of 2233 breast cancer mammograms are collected from different databases, including 1238 benign and 995 malignant, to evaluate and validate the proposed methodology. The ground truth labels have been collected by experts, and they diagnose those cases (labeled them) using experience as well as using different tests including the mammogram scan. Table 1 shows the distribution of mammogram images from the used datasets. During evaluation, images of each dataset are classified into two sets, namely training and testing sets.