# Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing

arXiv: https://arxiv.org/abs/2211.04573 · HTML: https://mlsanity.com/paper/2211.04573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Hyperparameters for Fine-tuning](https://mlsanity.com/paper/2002.11770/) (2020)
- [A Comparative Study on Polyp Classification using Convolutional Neural Networks](https://mlsanity.com/paper/2007.06071/) (2020)
- [Deep CNN Ensemble with Data Augmentation for Object Detection](https://mlsanity.com/paper/1506.07224/) (2015)

## Built on by (most cited first, 2 of 2)

- [Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks](https://mlsanity.com/paper/2304.13192/) (2023)
- [Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor](https://mlsanity.com/paper/2310.08398/) (2023)

## Abstract (arXiv)

In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively manufactured 48 types of realistic polyp phantoms with different hardness, type, and textures. Next, the performance of the used three ML algorithms in classifying the type of fabricated polyps was quantitatively evaluated using various statistical metrics.