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Colorectal cancer is a deadly disease that has become increasingly prevalentin recent years. Early detection is crucial for saving lives, but traditionaldiagnostic methods such as colonoscopy and biopsy have limitations. Colonoscopycannot provide detailed information within the tissues affected by cancer,while biopsy involves tissue removal, which can be painful and invasive. Inorder to improve diagnostic efficiency and reduce patient suffering, we studiedmachine-learningbased approach for colorectal tissue classification that usesacoustic resolution photoacoustic microscopy (ARPAM). With this tool, we wereable to classify benign and malignant tissue using multiple machine learningmethods. Our results were analyzed both quantitatively and qualitatively toevaluate the effectiveness of our approach.

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