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

• URL: http://arxiv.org/abs/2307.08556v1
• Date: Mon, 17 Jul 2023 15:15:26 GMT
• Title: Machine-Learning-based Colorectal Tissue Classification via Acoustic Resolution Photoacoustic Microscopy
• Authors: Shangqing Tong, Peng Ge, Yanan Jiao, Zhaofu Ma, Ziye Li, Longhai Liu, Feng Gao, Xiaohui Du, Fei Gao
• Abstract summary: Colonoscopy cannot provide detailed information within the tissues affected by cancer, while biopsy involves tissue removal, which can be painful and invasive. We studied machine-learning approach for colorectal tissue classification that uses acoustic resolution photoacoustic microscopy (ARPAM) Our results were analyzed both quantitatively and qualitatively to evaluate the effectiveness of our approach.
• Score: 3.916910844026426
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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