Comparison of Current Deep Convolutional Neural Networks for the Segmentation of Breast Masses in Mammograms

Andres Anaya-Isaza,Johan Manuel Cabrera-Chavarro,Leonel Mera-Jimenez,Diego Peluffo-Ordonez,Lorena Guachi-Guachi,Jorge Ivan Rios-Patino

doi:10.1109/access.2021.3127862

Abstract

Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.

Highlights

Breast cancer is a type of malignant tumor with the highest global incidence rate, accounting for approximately 10.4% of cancers [1]
We proposed a comparative analysis of 12 state-of-the-art deep learning (DL) networks under five loss functions to improve the automatic segmentation of breast examination images
The proposed convolutional models were built with the base UNet and the most recently developed networks with building blocks, squeeze-and-excitation blocks, residual connections, large numbers of deep layers, and novel architectures for segmentation or conventional classification, i.e., on problems other than medical imaging

Summary

Introduction

Breast cancer is a type of malignant tumor with the highest global incidence rate, accounting for approximately 10.4% of cancers [1]. It manifests as an excessive, disorganized, and invasive growth of breast cells [2]. Breast cancer is the leading cause of death among women between the ages of 20 and 50 years, and according to 2019 figures from the American Cancer Society, it estimated that there were approximately 268,600 new cases of invasive breast cancer, 48,100 cases of ductal carcinoma in situ (DCIS), and 41,740 deaths in the United States alone [1], [3].

Methods

Results

Conclusion