Improving Colon Carcinoma Grading by Advanced CNN Models

Marco Leo, Pierluigi Cacagnì, Luca Signore, Giulio Benincasa, Mikko O. Laukkanen, Cosimo Distante

Abstract: Cancer ranks as a leading cause of death and an important barrier to increasing life expectancy in every country of the world. For this reason, there is a great requirement for developing computer-aided approaches for accurate cancer diagnosis and grading that can overcome the problem of intra- and inter-observer inconsistency and thereby improve the accuracy and consistency in the cancer detection and treatment planning processes. In particular, the studies exploiting deep learning for automatic grading of colon carcinoma are still in infancy since the works in the literature did not exploit the most advanced models and methodologies of machine learning and systematic exploration of the most promising available convolutional networks is missing. To fill this gap, in this work, the most performing convolutional architectures in classification tasks have been exploited to improve colon carcinoma grading in histological images. The experimental proofs on the largest publicly available dataset demonstrated marked improvement with respect to the leading state of the art approaches.