# New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture

arXiv: https://arxiv.org/abs/2410.17735 · HTML: https://mlsanity.com/paper/2410.17735/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Deep learning with Elastic Averaging SGD](https://mlsanity.com/paper/1412.6651/) (2014)
- [Mixing ADAM and SGD: a Combined Optimization Method](https://mlsanity.com/paper/2011.08042/) (2020)
- [A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement](https://mlsanity.com/paper/1311.4033/) (2013)
- [Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks](https://mlsanity.com/paper/2103.14689/) (2021)

## Built on by (most cited first, 4 of 4)

- [Optimized Pap Smear Image Enhancement: Hybrid PMD Filter-CLAHE Using Spider Monkey Optimization](https://mlsanity.com/paper/2502.15156/) (2025)
- [Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE](https://mlsanity.com/paper/2506.15489/) (2025)
- [Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization](https://mlsanity.com/paper/2504.12807/) (2025)
- [Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification](https://mlsanity.com/paper/2610.11086/) (2026)

## Abstract (arXiv)

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.