DenseNet for Breast Tumor Classification in Mammographic Images
This work addresses breast cancer screening efficiency for medical professionals, but it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The study tackled the problem of automating breast lesion detection, segmentation, and classification in mammography images to reduce manual workload and errors, using a Mask-CNN with RoIAlign for feature extraction and DenseNet for classification, with results evaluated via cross-validation and AUC curve.
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.