An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading

Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-plannin...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-05, Vol.23 (9), p.4556
Main Authors: Carcagnì, Pierluigi, Leo, Marco, Signore, Luca, Distante, Cosimo
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Architecture
artificial intelligence
Cancer
Carcinoma
Classification
Colon
Colon cancer
colon carcinoma
Colonic Neoplasms - diagnosis
Colorectal cancer
Datasets
Deep Learning
Diagnosis
Early Detection of Cancer
ensembling
Female
histopathology
Humans
Investigations
Learning strategies
Machine learning
Medical diagnosis
Medical imaging equipment
Medical research
Neural networks
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Summary:Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common cancer worldwide and the second most common in women is colorectal cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Unfortunately, it has not yet been fully demonstrated how the most advanced models and methodologies of machine learning can impact this crucial task.This paper systematically investigates the use of advanced deep models (convolutional neural networks and transformer architectures) to improve colon carcinoma detection and grading from histological images. To the best of our knowledge, this is the first attempt at using transformer architectures and ensemble strategies for exploiting deep learning paradigms for automatic colon cancer diagnosis. Results on the largest publicly available dataset demonstrated a substantial improvement with respect to the leading state-of-the-art methods. In particular, by exploiting a transformer architecture, it was possible to observe a 3% increase in accuracy in the detection task (two-class problem) and up to a 4% improvement in the grading task (three-class problem) by also integrating an ensemble strategy.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23094556