التفاصيل البيبلوغرافية
| العنوان: |
An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading. |
| المؤلفون: |
Carcagnì, Pierluigi, Leo, Marco, Signore, Luca, Distante, Cosimo |
| المصدر: |
Sensors (14248220); May2023, Vol. 23 Issue 9, p4556, 17p |
| مصطلحات موضوعية: |
DEEP learning, MACHINE learning, CONVOLUTIONAL neural networks, COLON cancer diagnosis, COLON (Anatomy), CARCINOMA |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder