DenseNet for Breast Tumor Classification in Mammographic Images

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.

Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification

“…The primary advantage of fine-tuning is that it allows us to effectively train the model using a smaller amount of labeled data [23]. The features acquired by the pre-trained model are instrumental in classifying lung scans with high accuracy, even when confronted with limited training data and a relatively short training time [24].…”