YOLO and residual network for colorectal cancer cell detection and counting

The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep l...

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Published in:Heliyon
Main Author: Haq I.; Mazhar T.; Asif R.N.; Ghadi Y.Y.; Ullah N.; Khan M.A.; Al-Rasheed A.
Format: Article
Language:English
Published: Elsevier Ltd 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182881493&doi=10.1016%2fj.heliyon.2024.e24403&partnerID=40&md5=0ce6194fefcfd3891993820ba2afde92
id 2-s2.0-85182881493
spelling 2-s2.0-85182881493
Haq I.; Mazhar T.; Asif R.N.; Ghadi Y.Y.; Ullah N.; Khan M.A.; Al-Rasheed A.
YOLO and residual network for colorectal cancer cell detection and counting
2024
Heliyon
10
2
10.1016/j.heliyon.2024.e24403
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182881493&doi=10.1016%2fj.heliyon.2024.e24403&partnerID=40&md5=0ce6194fefcfd3891993820ba2afde92
The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models. © 2024
Elsevier Ltd
24058440
English
Article
All Open Access; Green Open Access
author Haq I.; Mazhar T.; Asif R.N.; Ghadi Y.Y.; Ullah N.; Khan M.A.; Al-Rasheed A.
spellingShingle Haq I.; Mazhar T.; Asif R.N.; Ghadi Y.Y.; Ullah N.; Khan M.A.; Al-Rasheed A.
YOLO and residual network for colorectal cancer cell detection and counting
author_facet Haq I.; Mazhar T.; Asif R.N.; Ghadi Y.Y.; Ullah N.; Khan M.A.; Al-Rasheed A.
author_sort Haq I.; Mazhar T.; Asif R.N.; Ghadi Y.Y.; Ullah N.; Khan M.A.; Al-Rasheed A.
title YOLO and residual network for colorectal cancer cell detection and counting
title_short YOLO and residual network for colorectal cancer cell detection and counting
title_full YOLO and residual network for colorectal cancer cell detection and counting
title_fullStr YOLO and residual network for colorectal cancer cell detection and counting
title_full_unstemmed YOLO and residual network for colorectal cancer cell detection and counting
title_sort YOLO and residual network for colorectal cancer cell detection and counting
publishDate 2024
container_title Heliyon
container_volume 10
container_issue 2
doi_str_mv 10.1016/j.heliyon.2024.e24403
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182881493&doi=10.1016%2fj.heliyon.2024.e24403&partnerID=40&md5=0ce6194fefcfd3891993820ba2afde92
description The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models. © 2024
publisher Elsevier Ltd
issn 24058440
language English
format Article
accesstype All Open Access; Green Open Access
record_format scopus
collection Scopus
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