Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps versus Resected Polyps

Adriel Abraham
,
Rejath Jose
ORCID logo ,
Jawad Ahmad
,
Jai Joshi
,
Thomas Jacob
,
Aziz-ur-rahman Khalid
,
Hassam Ali
,
Pratik Patel
,
Jaspreet Singh
,
Milan Toma
* ORCID logo
Version 1 : Received: 7 August 2023 / Approved: 8 August 2023 / Online: 9 August 2023 (08:51:59 CEST)

A peer-reviewed article of this Preprint also exists.

Abraham, A.; Jose, R.; Ahmad, J.; Joshi, J.; Jacob, T.; Khalid, A.-U.-R.; Ali, H.; Patel, P.; Singh, J.; Toma, M. Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps. J. Imaging 2023, 9, 215. Abraham, A.; Jose, R.; Ahmad, J.; Joshi, J.; Jacob, T.; Khalid, A.-U.-R.; Ali, H.; Patel, P.; Singh, J.; Toma, M. Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps. J. Imaging 2023, 9, 215.

Abstract

(1) Background: Colon polyps are common protrusions in the colon’s lumen with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine-learning image classification models’ performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOV8), in the detection and classification of colon polyps were evaluated. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models’ ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the `normal’ class, 0.97 - 1.00 for `polyps,’ and 0.97 - 1.00 for `resected polyps.’ While GTM exclusively classified images into these three categories, both YOLOV8 and RF3 extended their capabilities to detect normal colonic tissue, polyps, and resected polyps, with YOLOV8 and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Keywords

Machine; Learning; Models; Image; Detection; Colonic; Polyps; Resected

Subject

Medicine and Pharmacology, Gastroenterology and Hepatology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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