Journal ArticleDOI
Probabilistic neural network for breast cancer classification
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TLDR
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 and PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively.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.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.
TL;DR: A general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers is provided.
Journal ArticleDOI
Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques
Md. Milon Islam,Md. Rezwanul Haque,Hasib Iqbal,Md. Munirul Hasan,Mahmudul Hasan,Muhammad Nomani Kabir +5 more
TL;DR: Five supervised machine learning techniques named support vector machine (SVM), K-nearest neighbors, random forests, artificial neural networks (ANNs) and logistic regression are compared and it is revealed that the ANNs obtained the highest accuracy, precision, and F1 score.
Proceedings ArticleDOI
Prediction of breast cancer using support vector machine and K-Nearest neighbors
TL;DR: A novel modality for the prediction of breast cancer is presented and introduces with the Support Vector Machine and K-Nearest Neighbors which are the supervised machine learning techniques for breast cancer detection by training its attributes.
Journal ArticleDOI
Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network
Maciej Kusy,Roman Zajdel +1 more
TL;DR: New methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN) based on three reinforcement learning algorithms, based on Q(0)-learning, Q(λ-learning, and stateless Q-learning are proposed.
References
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Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.