Inter – intra observer variability using deep learning and traditional image processing for breast cancer

Breast cancer is one of the most common life-threatening diseases that affects women globally. Saudi Arabia is also one of the countries that suffer from a serious number of this disease among women. In terms of diagnosis modalities, a mammogram is the first line for detecting breast cancer. In addition, breast cancer can be screened by real-time ultrasound images, which are of relatively less quality and have more impact (noninvasive) images. Therefore, the purpose of this study is to develop image enhancement techniques using deep learning and image processing techniques. The main goal is to improve the ultrasound images in order to help radiologists screen the disease more accurately. For this study, ninety female patients of ages between 15 – 77 years are considered. These patients were already diagnosed using ultrasound with breast lesions. The images are visually graded and evaluated by two trained radiologists, both pre- and post-enhancement. In particular, two parameters were considered; 1) BI-RAD categories, 2) Breast cancer classification. The agreement between radiologists and post-enhancement was assessed using simple kappa and weighted kappa statistics. Moreover, sensitivity and specificity are also calculated.