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Histological evaluation of tissue samples is a typical approach to identifycolorectal cancer metastases in the peritoneum. For immediate assessment,reliable and real-time in-vivo imaging would be required. For example,intraoperative confocal laser microscopy has been shown to be suitable fordistinguishing organs and also malignant and benign tissue. So far, theanalysis is done by human experts. We investigate the feasibility of automaticcolon cancer classification from confocal laser microscopy images using deeplearning models. We overcome very small dataset sizes through transfer learningwith state-of-the-art architectures. We achieve an accuracy of 89.1% for cancerdetection in the peritoneum which indicates viability as an intraoperativedecision support system.

Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks