Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Abstract

Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagnosis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms. Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems. The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women's Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset. It contains 780 images (133 normal, 437 benign and 210 malignant). While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant). To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers. Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification. We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation. The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.

A Novel Neural Network Method for Face Recognition With a Single Sample Per Person

“…We will solve this problem using Generative Adversarial Network(GAN) by generating images of the same person in different years. Furthermore, we would like to enlarge the dataset by using artificial data augmentation techniques as used in [44].…”

Dataset of breast ultrasound images

“…Data source location Baheya Hospital for Early Detection & Treatment of Women's Cancer, Cairo, Egypt. Data accessibilityhttps://scholar.cu.edu.eg/?q=afahmy/pages/datasetRelated research articleWalid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled and Aly Fahmy, Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images [1]…”

“…Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled and Aly Fahmy, Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images [1]…”

Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network

“…In a previous study [62] dealing with the synthesis of brain-structured MRI, a GAN structure that appropriately augments the input image domain is proposed, and some related studies comparing the performance of each generalization when the training steps of various classifiers were enhanced using generated images have been reported [27,28,32,[63][64][65]. These previous studies on GAN-based DA in the medical imaging field have emphasized the design of the applied GAN and the improved generalization of the target model that was trained using the augmented dataset.…”