MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network

Breast cancer is cancer that develops from the breast tissue and has been recognized as one of the most dangerous and deadly diseases that is the second leading cause of cancer deaths in women. To help doctors and radiologists to diagnose these tumors as well as decrease the time and increase the accuracy, many machine learning methods have been implemented by now. Most of these methods suffer from extracting some significant features that represent the boundary of tumors. This is due to the fact that benign and malignant tumors can be considered the same if some borders cannot segment properly. So, in this study, we propose an automatic breast tumor segmentation and recognition based on a shallow convolutional neural network that uses multi-feature extraction routes. Also, an image enhancement approach is used before applying the image into the model which leads to avoiding a very deep structure. Our strategy leads to improvement in detecting the border of tumors and boosts the classification accuracy of tumors. We evaluated our pipeline on Mammographic Image Analysis Society (Mini-MIAS) and Digital Database for Screening Mammography (DDSM) datasets. The developed model can localize and classify tumors with the accuracy of 0.936, 0.890, 0.871 on the DDSM, and 0.944, 0.915, 0.892 on the Mini-MIAS, for normal, benign, and malignant regions, respectively.

Automated summary

Breast cancer is a dangerous and deadly disease. We propose an automatic breast tumor segmentation and recognition method based on a shallow convolutional neural network that can improve the accuracy of tumor border detection and the classification accuracy of tumors.