AUTOMATED BREAST ULTRASOUND LESIONS DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e. Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

Existing System

Radial Gradient Index (RGI) Filtering and Multifractal Filtering.

Proposed System

This paper investigated the use of three deep learning approaches (Patch-based LeNet, U-Net, Transfer Learning FCN-AlexNet) and a comprehensive evaluation of the most representative lesion detection methodologies for breast ultrasound lesion detection. The performances were evaluated on two datasets in terms of TPF, FPs/image and F-measure. Amongst the different methodologies discussed in this paper, the Transfer Learning FCN-AlexNet achieved the best results for Dataset A and the proposed Patch-based LeNet obtained the best results for Dataset B in terms of FPs/image and F-measure. DPM and deep learning methods are adaptable to the specific characteristics of any dataset, since these are machine-learning based and a particular model is constructed for each dataset. However, the limitation of such methods is that they require a training process and negative images in the experiment. For further research, it is our assertion that deep learning approaches could be adapted to other medical imaging techniques such as 3 dimensional ultrasound or elastography.  

Architecture

BLOCK DIAGRAM