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.
Highlights
Being a disease with fatal outcomes, breast cancer is considered as the second most common cancer type among females around the world
A new type of approach is proposed with the purpose of improving the accuracy of breast cancer diagnosis by means of one of the techniques found in the concept of machine learning that is called deep learning
The results were provided with the utilization of techniques of Confusion Matrix, accuracy values (ACC), Receiver Operator Characteristic Curve (ROC) curve and area under the ROC curve (AUC) which were used in the performance evaluation of the model designed with deep learning for enhancement of breast cancer diagnosis accuracy
Summary
Being a disease with fatal outcomes, breast cancer is considered as the second most common cancer type among females around the world. In Turkey, twenty-five thousand patients are diagnosed with breast cancer each year. This rate continues to increase in developing countries. For this reason, the importance of accurate and early diagnosis has become higher than ever. The rate of machine learning usage in diagnosis of diseases within the field of medicine is growing. These techniques provide assistance to physicians in achieving an early and accurate diagnosis. A new type of approach is proposed with the purpose of improving the accuracy of breast cancer diagnosis by means of one of the techniques found in the concept of machine learning that is called deep learning
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