Robust Breast cancer Detection using Faster R-CNN Algoritma

Abstract

One of the most common screening tools for breast cancer detection is ultrasound. However, the lack of qualified radiologists causes the diagnosis process to become a challenging task. Deep learning's promising achievement in various computer vision problems inspires us to apply the technology to medical image recognition problems. We propose a detection model based on the Faster R-CNN to detect breast cancer quickly and accurately. We conduct this experiment by collecting breast cancer datasets, conducting pre-processing, training models, and evaluating the model performance. Based on the experiment result, we obtain that this model can detect breast cancer with bounding boxes. In this model, it is possible to detect the bounding box that is more than what it should be, so we applied NMS to eliminate the prediction of the bounding box that is less precise to increase accuracy.