CALCIFICATION CLUSTERS AND LESIONS ANALYSIS IN MAMMOGRAM USING MULTI-ARCHITECTURE DEEP LEARNING ALGORITHMS

Abstract

Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGG16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.