Robust Breast cancer Detection using Faster R-CNN Algoritma

  • Anisa Dian Pratiwi Universitas Respati Yogyakarta
  • Irma Permata Sari Universitas Negeri Jakarta


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.


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How to Cite
PRATIWI, Anisa Dian; SARI, Irma Permata. Robust Breast cancer Detection using Faster R-CNN Algoritma. International Journal of Informatics and Computation, [S.l.], v. 4, n. 1, p. 43-53, oct. 2022. ISSN 2714-5263. Available at: <>. Date accessed: 02 dec. 2022. doi: