An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (NEUBREA)

Title:

An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (NEUBREA)

Creator:

Bayram , Bulent, Koca, Hilmi K., Narin , Burcu, Cavdaroglu, G. Cigdem, Celik, Levent, Acar, Ugur, and Cubuk , Rahmi

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:b54e396f-d685-45b2-8ac4-d43290c8bd22
uuid:b54e396f-d685-45b2-8ac4-d43290c8bd22
doi:10.14311/NNW.2013.23.030

Subject:

Computer aided detection, medical image processing, breast cancer, and ANN

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

diagnosis. Moreover various studies can be found in medical journals dedicated to Artificial Neural Networks (ANN). In the presented study, a method was developed to learn and detect benign and malignant tumor types in contrast-enhanced breast magnetic resonance images (MRI). The backpropagation algorithm was taken as the ANN learning algorithm. The algorithm (NEUBREA) was developed in C# programming language by using Fast Artificial Neural Network Library (FANN). Having been diagnosed by radiologists, 7 cases of malignant tumor, 8 cases of benign tumor, and 3 normal cases were used as a training set. The results were tested on 34 cases that had been diagnosed by radiologists. After the comparison of the results, the overall accuracy of algorithm was defined as 92%.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2013 Volume:23 | Number:5

Harvested from:

CDK

Metadata only:

false