| dc.description.abstract |
Breast cancer is a very important public health problem because mortality and morbidity tend to increase every year. This problem occurs because breast cancer is not detected early. Technology is needed to detect breast cancer early so as to reduce the risk of death in women, and increase life expectancy. Technology that can be used to recognize certain objects in detection requires data that represents the object to detect the disease in the form of digital images. The digital image is processed so that it can detect cancer. One method that can be used in digital image processing to detect cancer is using the artificial neural network method. One type of artificial neural network used is Convolutional Neural Networks (CNN). Therefore, this research will carry out Fine Tuning Convulation Neural Network using MobileNet architecture to detect breast cancer using the Adam, MSProp, SGD optimization function. And the results of this research will show the success rate of the SGD optimization function which has the best validation accuracy in detecting breast cancer.
Key Word : Convolutional Neural Networks (CNN), MobileNet, Adam, MSProp, SGD.
| en_US |
