Evaluating Convolution Neural Network optimization Algorithms for Classification of Cervical Cancer Macro Images

Abstract

In this study, Convolution Neural Network (CNN)-based learning method, which is well-used in deep learning is applied to evaluate optimization algorithms for improved accuracy in cervix cancer detection. The convolutional layer in this study is made up of numerous convolution kernels which are used to compute different feature maps for representations of the inputs. As a result, the architecture of the model consisting of convolutional layers, pooling layers, and fully-connected layers is designed by stacking the network layers. The model is evaluated firstly by increasing the amount of image data through image augmentation. Furthermore, the hyper parameters for optimum performance are chosen. Finally, analysis is performed for Stochastic gradient descent (SGD), Root Mean Square Propagation (RMSprop) and Adaptive Moment Estimation (Adam) optimizers to determine which improves the networks performance for the classification of cervix cancer.