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

Overview of attention for article published in Computational & Mathematical Methods in Medicine, April 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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2 X users
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1 patent

Citations

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107 Dimensions

Readers on

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176 Mendeley
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Title
Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer
Published in
Computational & Mathematical Methods in Medicine, April 2017
DOI 10.1155/2017/2610628
Pubmed ID
Authors

M. M. Mehdy, P. Y. Ng, E. F. Shair, N. I. Md Saleh, C. Gomes

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 175 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 27 15%
Student > Ph. D. Student 24 14%
Student > Master 22 13%
Researcher 12 7%
Lecturer 8 5%
Other 32 18%
Unknown 51 29%
Readers by discipline Count As %
Computer Science 49 28%
Engineering 33 19%
Medicine and Dentistry 10 6%
Biochemistry, Genetics and Molecular Biology 5 3%
Immunology and Microbiology 4 2%
Other 18 10%
Unknown 57 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 January 2024.
All research outputs
#7,280,570
of 25,593,129 outputs
Outputs from Computational & Mathematical Methods in Medicine
#178
of 1,419 outputs
Outputs of similar age
#108,318
of 324,203 outputs
Outputs of similar age from Computational & Mathematical Methods in Medicine
#3
of 10 outputs
Altmetric has tracked 25,593,129 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,419 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 324,203 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.