Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2017:2017:2610628.
doi: 10.1155/2017/2610628. Epub 2017 Apr 3.

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

M M Mehdy  1 , P Y Ng  1 , E F Shair  2 , N I Md Saleh  3 , C Gomes  2
Affiliations
Review

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

M M Mehdy et al. Comput Math Methods Med. 2017.

Abstract

Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Structure of a typical ANN for classification of breast tumors in mammography [12].
Figure 2
Figure 2
Results (from (a)–(c)): original image, image after first stage of NN processing, and image at second stage of NN processing using Gabor wavelets as input for mammogram image [20].
Figure 3
Figure 3
Segmentations of cysts for breast ultrasound image using ANN [32].
Figure 4
Figure 4
Multistate CNN used to segment small fatty breast and medium dense breast for MRI image [39].
See this image and copyright information in PMC

References

    1. Hadjiiski L., Sahiner B., Helvie M. A., et al. Breast masses: computer-aided diagnosis with serial mammograms. Radiology. 2006;240(2):343–356. doi: 10.1148/radiol.2401042099. - DOI - PubMed
    1. Balakumaran T., Vennila I. L. A., Shankar C. G. Detection of microcalcification in mammograms using wavelet transform and fuzzy shell clustering. International Journal of Computer Science and Information Technology. 2010;7(1):121–125.
    1. Dheeba J., Albert Singh N., Tamil Selvi S. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. Journal of Biomedical Informatics. 2014;49:45–52. doi: 10.1016/j.jbi.2014.01.010. - DOI - PubMed
    1. Lo S.-C. B., Chan H.-P., Lin J.-S., Li H., Freedman M. T., Mun S. K. Artificial convolution neural network for medical image pattern recognition. Neural Networks. 1995;8(7-8):1201–1214. doi: 10.1016/0893-6080(95)00061-5. - DOI
    1. Da C., Zhang H., Sang Y. Brain CT image classification with deep neural networks. Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems; 2015; pp. 653–662. - DOI

MeSH terms