DenseNet for Breast Tumor Classification in Mammographic Images

Autor:	Gaona, Yuliana Jiménez, Rodriguez-Alvarez, María José, Morató, Hector Espinó, Malla, Darwin Castillo, Lakshminarayanan, Vasudevan
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning I.4.9
Druh dokumentu:	Working Paper
Popis:	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. Comment: to be submitted to The 2nd International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.09637 Zobrazit plný text záznamu View this record from Arxiv