Authors:
Francesco Ponzio
;
Enrico Macii
;
Elisa Ficarra
and
Santa Di Cataldo
Affiliation:
Politecnico di Torino, Italy
Keyword(s):
Colorectal Cancer, Histological Image Analysis, Convolutional Neural Networks, Deep Learning, Transfer Learning, Pattern Recognition.
Abstract:
The analysis of histological samples is of paramount importance for the early diagnosis of colorectal cancer
(CRC). The traditional visual assessment is time-consuming and highly unreliable because of the subjectivity
of the evaluation. On the other hand, automated analysis is extremely challenging due to the variability of
the architectural and colouring characteristics of the histological images. In this work, we propose a deep
learning technique based on Convolutional Neural Networks (CNNs) to differentiate adenocarcinomas from
healthy tissues and benign lesions. Fully training the CNN on a large set of annotated CRC samples provides
good classification accuracy (around 90% in our tests), but on the other hand has the drawback of a very
computationally intensive training procedure. Hence, in our work we also investigate the use of transfer
learning approaches, based on CNN models pre-trained on a completely different dataset (i.e. the ImageNet).
In our results, transfer l
earning considerably outperforms the CNN fully trained on CRC samples, obtaining
an accuracy of about 96% on the same test dataset.
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