Different breast cancer detection systems have been developed to help clinicians analyze screening mammograms. Breast cancer has been increasing gradually so scientists work to develop new methods to reduce the risks of this life-threatening disease. Convolutional Neural Networks (CNNs) have shown much promise In the field of medical imaging because of recent developments in deep learning. However, CNN’s based methods have been restricted due to the small size of the few public breast cancer datasets. This research has developed a new framework and introduced it to detect breast cancer. This framework utilizes Convolutional Neural Networks (CNNs) and image processing to achieve its goal because CNNs have been an important success in image recognition, reaching human performance. An efficient and fast CNN pre-trained object detector called RetinaNet has been used in this research. RetinaNet is an uncomplicated one-stage object detector. A two-stage transfer learning has been used with the selected detector to improve the performance. RetinaNet model is initially trained with a general-purpose dataset called COCO dataset. The transfer learning is then used to apply the RetinaNet model to another dataset of mammograms called the CBIS-DDSM dataset. Finally, the second transfer learning is used to test the RetinaNet model onto a small dataset of mammograms called the INbreast dataset. The results of the proposed two-stage transfer learning (RetinaNet → CBIS-DDSM → INbreast) are better than the other state-of-the-art methods on the public INbreast dataset. Furthermore, the True Positive Rate (TPR) is 0.99 ± 0.02 at 1.67 False Positives per Image (FPPI), which is better than the one-stage transfer learning with a TPR of 0.94 ± 0.02 at 1.67 FPPI.

One of the most prolonging and monotonous procedures entails bank procedures. In most cases, bank users spend much time completing tasks that would otherwise be performed online. It is also evident that with the evolution of technology, significant changes have been felt. For example, May bank came up with its website. With the emergence of Smartphone, the institution has also had to establish its mobile application. The main aim of designing the mobile application has been to attend to the institution’s service users timely. As more and more technologies emerge, developing enterprises have had to keep abreast and the chief motivation has been to steer overall satisfaction among users; relative to the respective applications. This study was based in Sinton and involved 15 performances. The participants engaged in four tasks. From the study’s findings, it is evident that the majority of the participants were satisfied with the application’s functions. Despite this promising outcome, it was established that some participants experienced significant challenges; upon which they had to correct any mistakes that emerged (as the experiment progressed).