Machine-Learning-based Colorectal Tissue Classification via Acoustic Resolution Photoacoustic Microscopy

TL;DR

This paper presents a machine learning approach using acoustic resolution photoacoustic microscopy to classify colorectal tissues as benign or malignant, aiming to improve early detection and reduce invasive procedures.

Contribution

It introduces a novel application of ARPAM combined with machine learning for non-invasive colorectal tissue classification, enhancing diagnostic accuracy.

Findings

Successful classification of benign and malignant tissues

Quantitative and qualitative analysis confirms effectiveness

Potential for improved early detection of colorectal cancer

Abstract

Colorectal cancer is a deadly disease that has become increasingly prevalent in recent years. Early detection is crucial for saving lives, but traditional diagnostic methods such as colonoscopy and biopsy have limitations. Colonoscopy cannot provide detailed information within the tissues affected by cancer, while biopsy involves tissue removal, which can be painful and invasive. In order to improve diagnostic efficiency and reduce patient suffering, we studied machine-learningbased approach for colorectal tissue classification that uses acoustic resolution photoacoustic microscopy (ARPAM). With this tool, we were able to classify benign and malignant tissue using multiple machine learning methods. Our results were analyzed both quantitatively and qualitatively to evaluate the effectiveness of our approach.