access icon openaccess Breast mass classification method based on convolutional neural networks

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 11/10/2019, Accepted 19/11/2019, Published 31/01/2020

To classify the X-ray mammograms images as benign or malignant is a long-standing unresolved problem, due to the high similarity of different between the mammograms images. In this study, a novel convolutional neural network based X-ray breast mass classification method is proposed. The method receives original breast mass image and its transformed image simultaneously, and extracts more abundant features from the breast mass images. Though the transformed image is a simple inverse of the original image, it allows another side of the thing to be perceived by the network at a very low cost. Experiment results demonstrate that the proposed method significantly outperforms the compared state-of-the-art classification methods for breast mass.

Inspec keywords: image segmentation; medical image processing; mammography; convolutional neural nets; cancer; feature extraction; image classification

Other keywords: original breast mass image; X-ray breast mass classification method; breast mass images; convolutional neural networks; X-ray mammograms images; transformed image

Subjects: X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Biology and medical computing; X-rays and particle beams (medical uses); Computer vision and image processing techniques; Neural computing techniques; Patient diagnostic methods and instrumentation; Image recognition

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