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In this paper, we have implemented, using Matlab Simulink an analog artificial neural network for breast cancer classification. Simulated results with ideal building blocks exhibit a total error of classification of 2.6%. Thanks to this value, we have modified Simulink models of the building blocks (i.e. multiplier, activation function and its derivative) in order to take into account their non-idealities...
Early detection of cancer or any disease is among the main keys to its cure. One of the state of the art methods in cancer detection is machine learning, namely ANNs (Artificial Neural Networks). ANNs have proved to be efficient due to their ability to learn and generalize from data. This paper proposes a low-complexity architecture of an ANN that classifies breast cancer as either Benign or Malignant...
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