Machine-Learning-based Colorectal Tissue Classification via Acoustic Resolution Photoacoustic Microscopy
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 b...
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| Veröffentlicht in: | arXiv.org 2023-07 |
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| creator | Tong, Shangqing Ge, Peng Jiao, Yanan Ma, Zhaofu Li, Ziye Liu, Longhai Gao, Feng Du, Xiaohui Gao, Fei |
| description | 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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| subjects | Acoustic microscopy Cancer Classification Machine learning Photoacoustic microscopy |
| title | Machine-Learning-based Colorectal Tissue Classification via Acoustic Resolution Photoacoustic Microscopy |
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