  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.
