Abstract:

Breast cancer is a highly fatal disease that is very prevalent among the female population. In this study, a new type of approach is proposed with the aim of improving the accuracy of breast cancer diagnosis, an important problem of our present time, by means of deep learning, one of the techniques in machine learning. In the designed method, the original data set of Breast Cancer Wisconsin being available in the Irvine Machine Learning Repository of University of California was used. Within this data set, there were 699 data consisting of 10 independent variables and 1 dependent variable. The complete utilization of the entire data set was ensured by correction of 16 incorrect data. A normalization process was applied in the data set for the purpose of reducing the time required for learning process. The used data set was allocated as 80% for training, 10% for validation, and 10% for testing. An artificial neural network was designed for the deep learning model. The neural network was set up of a total of 5 layers which were an input layer with 10 neurons, 3 hidden layers with 1000 neurons for each layer, and an output layer with 3 neurons. The software, developed for implementation was written by using Spyder which is an interactive development environment for Python programming language. In addition, Keras neural network API was used. The performance of the model was evaluated with Confusion Matrix and ROC (Receiver Operating Characteristic) analysis. According to the test data obtained at the end of the training, it was observed that the implemented model provided successful results. It is considered that the proposed method will contribute to the improvement of breast cancer diagnosis accuracy.

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