Diagnosis of Malignancy in Thyroid Tumors by Multi-Layer Perceptron Neural Networks With Different Batch Learning Algorithms


  •  Saeedeh Pourahmad    
  •  Mohsen Azad    
  •  Shahram Paydar    

Abstract

To diagnose the malignancy in thyroid tumor, neural network approach is applied and the performances of thirteen batch learning algorithms are investigated on accuracy of the prediction. Therefore, a back propagation feed forward neural networks (BP FNNs) is designed and three different numbers of neuron in hidden layer are compared (5, 10 and 20 neurons). The pathology result after the surgery and clinical findings before surgery of the patients are used as the target outputs and the inputs, respectively. The best algorithm(s) is/are chosen based on mean or maximum accuracy values in the prediction and also area under Receiver Operating Characteristic Curve (ROC curve). The results show superiority of the network with 5 neurons in the hidden layer. In addition, the better performances are occurred for Polak-Ribiere conjugate gradient, BFGS quasi-newton and one step secant algorithms according to their accuracy percentage in prediction (83%) and for Scaled Conjugate Gradient and BFGS quasi-Newton based on their area under the ROC curve (0.905).



This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1916-9736
  • Issn(Onlne): 1916-9744
  • Started: 2009
  • Frequency: monthly

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Google-based Impact Factor (2017): 1.84

h-index (June 2018): 32

i10-index (June 2018): 105

h5-index (June 2018): 23

h5-median(June 2018): 28

RG Journal impact: 1.26

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