Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection

TL;DR

This study compares various deep learning models, especially CNNs with transfer learning, to improve polyp detection accuracy in colonoscopy images, achieving up to 98% accuracy.

Contribution

It identifies the most effective CNN-based transfer learning model for polyp detection with high accuracy, emphasizing minimal-layer models.

Findings

Achieved 98% accuracy with selected CNN model.

Transfer learning outperforms traditional CNNs in this task.

Minimal 4-layer CNN models can be highly effective.

Abstract

This paper is created to explore deep learning models and algorithms that results in highest accuracy in detecting polyp on colonoscopy images. Previous studies implemented deep learning using convolution neural network (CNN) algorithm in detecting polyp and non-polyp. Other studies used dropout, and data augmentation algorithm but mostly not checking the overfitting, thus, include more than four-layer modelss. Rulei Yu et.al from the Institute of Software, Chinese Academy of Sciences said that transfer learning is better talking about performance or improving the previous used algorithm. Most especially in applying the transfer learning in feature extraction. Series of experiments were conducted with only a minimum of 4 CNN layers applying previous used models and identified the model that produce the highest percentage accuracy of 98% among the other models that apply transfer learning. Further studies could use different optimizer to a different CNN modelsto increase accuracy.