DenseNet for Breast Tumor Classification in Mammographic Images

TL;DR

This paper presents a deep learning approach combining Mask-CNN and DenseNet architectures for automatic detection, segmentation, and classification of breast tumors in mammographic images, aiming to improve diagnostic accuracy and efficiency.

Contribution

It introduces a novel deep convolutional neural network method integrating Mask-CNN and DenseNet for enhanced breast tumor analysis in mammography images.

Findings

High precision and accuracy demonstrated by cross-validation

Effective tumor localization in mammographic images

Improved diagnostic efficiency for breast cancer detection

Abstract

Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to features selection and extraction; and the classification was carried out by DenseNet architecture. Finally, the precision and accuracy of the model is evaluated by cross validation matrix and AUC curve. To summarize, the findings of this study may provide a helpful to improve the diagnosis and efficiency in the automatic tumor localization through the medical image classification.