# Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks

## TL;DR

This study explores the potential of using deep learning models to automatically detect colon cancer in confocal laser microscopy images, aiming for real-time intraoperative diagnosis.

## Contribution

It demonstrates the feasibility of automatic colon cancer classification in confocal microscopy images using transfer learning with deep neural networks.

## Key findings

- Achieved 89.1% accuracy in cancer detection
- Overcame small dataset limitations with transfer learning
- Supports intraoperative decision-making

## Abstract

Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum. For immediate assessment, reliable and real-time in-vivo imaging would be required. For example, intraoperative confocal laser microscopy has been shown to be suitable for distinguishing organs and also malignant and benign tissue. So far, the analysis is done by human experts. We investigate the feasibility of automatic colon cancer classification from confocal laser microscopy images using deep learning models. We overcome very small dataset sizes through transfer learning with state-of-the-art architectures. We achieve an accuracy of 89.1% for cancer detection in the peritoneum which indicates viability as an intraoperative decision support system.