New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture

TL;DR

This study evaluates various optimizers for CNN architectures in classifying cervical cancer Pap smear images, finding Adamax as the most effective optimizer across tested models, and highlights the impact of transfer learning on performance.

Contribution

The paper provides new insights into optimal CNN configurations and optimizer choices for cervical cancer Pap smear image classification using transfer learning.

Findings

Adamax achieved the highest accuracy among optimizers.

Transfer learning improved CNN performance regardless of optimizer.

Optimizer choice had minimal impact when using transfer learning.

Abstract

The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), RMSprop, Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.