You are in:Home/Publications/ Azar AT, El-Said SA (2013). Probabilistic neural network for breast cancer classification. Neural Computing and Applications, 23(6): 1737-1751. Springer. DOI: 10.1007/s00521-012-1134-8 [ISI Indexed: Impact Factor: 2.505].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Azar AT, El-Said SA (2013). Probabilistic neural network for breast cancer classification. Neural Computing and Applications, 23(6): 1737-1751. Springer. DOI: 10.1007/s00521-012-1134-8 [ISI Indexed: Impact Factor: 2.505].
Authors: Azar AT, El-Said SA
Year: 2013
Keywords: Not Available
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Local/International: International
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Abstract:

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.

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