Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection
This work addresses polyp detection in medical imaging, but it is incremental as it builds on existing CNN and transfer learning methods without major innovations.
The paper explored various deep learning models to improve accuracy in automatic polyp detection from colonoscopy images, achieving 98% accuracy with a model using at least 4 CNN layers and transfer learning.
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.