<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.</p></abstract>

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

Department of Electrical & Electronics Engineering

Department of Electronics & Communication Engineering

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR...

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

Online veröffentlicht: 21. Dez. 2017

Seitenbereich: 81 - 88

Eingereicht: 29. Mai 2017

Akzeptiert: 26. Sept. 2017

DOI: https://doi.org/10.1515/pjmpe-2017-0014

Schlüsselwörter
Breast DCE-MR Images, lesion classification, classifier, Artificial Neural Networks, Artificial fishes swarm algorithm

© 2017 D Janaki Sathya et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

Online veröffentlicht: 21. Dez. 2017

Seitenbereich: 81 - 88

Eingereicht: 29. Mai 2017

Akzeptiert: 26. Sept. 2017

DOI: https://doi.org/10.1515/pjmpe-2017-0014

SchlüsselwörterBreast DCE-MR Images, lesion classification, classifier, Artificial Neural Networks, Artificial fishes swarm algorithm

© 2017 D Janaki Sathya et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Breast DCE-MR Images, lesion classification, classifier, Artificial Neural Networks, Artificial fishes swarm algorithm