内科: オープンアクセス
オープンアクセス

概要

Classification and Segmentation of Breast Tumor Using Mask R-CNN on Mammograms

Syed Kazim Raza, Syed Shameer Sarwar, Saad Muhammad Syed, Najeed Ahmed Khan

Purpose: Breast cancer has caused more deaths in women compared to any other cancer that might be found among women. With that being said, this research has proposed a method which can detect classify and segment the different types of breast tumors. This paper has also discussed the different methods by which the breast cancer has been classified and segmented in the past.

Method: Breast cancer can be detected in its early stages by MRI and/or mammography of the breast muscles. For this research a novel approach is proposed for breast cancer detection, classification and segmentation. The proposed framework uses breast mammograms from the CBIS-DDSM (Curated Breast Imaging Subset of DDSM) DICOM images. Mammograms are radio images of a muscle. The DICOM data has been preprocessed in such a way that it could be incorporated with more traditional format, and then the patches from the mammogram images have been taken out and finally fed into the mask RCNN neural network.

Results: The outcome of the approach is, that the proposed framework is able to localize cancer tumor, even when it has developed in multiple regions, making it a multi-class classifier. The framework is also able to classify whether the tumor is benign or malignant as well as segments the cancerous tumor region with a pixel wise annotation. The average accuracy observed is about 85% on test cases, with precision value of 0.75, recall being 0.8 and F1 score 0.825.

Conclusion: The proposed framework is cost efficient and can be used as a helping tool for the radiologist in breast cancer detection. In future the proposed approach can also be implemented on other cancerous tumors for classification and segmentation purposes.