Breast cancer is one of the most prevalent forms of cancer affecting women. Detection of suspicious lesions on mammographic images is considered a challenging task due to the variability of lesion size sand shapes, the problematic margins of the findings, and some extremely small lesions that are difficult to localize. With the increasing availability of digitized clinical archives and the development of complex deep learning (DL) methods, we are witnessing a trend towards the integration of robust computer-aided detection (CAD) systems to assist in the automatic segmentation of lesions on mammograms to aid in the diagnosis of breast cancer. This study presents deep learning–based automatic detection algorithm (DLAD), directly implemented in picture archiving and communication system (PACS) to aid in improving the radiologist’s workflow. The proposed DLAD is evaluated on INbreast dataset with a sample size of n=138 (71 [51.45%] BI-RADS 4/5/6 images, 67 [48.55%]BI-RADS 1 images). Preliminary results show a sensitivity of 0.9296 [95%CI 0.8701-0.9891], specificity of 0.7273 [0.6207-0.8339] and IoU of 0.5661, indicating a low false negative rate while maintaining a reasonable false positive rate.