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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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024004341 |
| _version_ | 1797328676012949504 |
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| author | Inayatul Haq Tehseen Mazhar Rizwana Naz Asif Yazeed Yasin Ghadi Najib Ullah Muhammad Amir Khan Amal Al-Rasheed |
| author_facet | Inayatul Haq Tehseen Mazhar Rizwana Naz Asif Yazeed Yasin Ghadi Najib Ullah Muhammad Amir Khan Amal Al-Rasheed |
| author_sort | Inayatul Haq |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-08T06:55:07Z |
| format | Article |
| id | doaj.art-8fa8f048c8e64dd9bda312b9725ee90d |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T06:55:07Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-8fa8f048c8e64dd9bda312b9725ee90d2024-02-03T06:37:19ZengElsevierHeliyon2405-84402024-01-01102e24403YOLO and residual network for colorectal cancer cell detection and countingInayatul Haq0Tehseen Mazhar1Rizwana Naz Asif2Yazeed Yasin Ghadi3Najib Ullah4Muhammad Amir Khan5Amal Al-Rasheed6School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, ChinaDepartment of Computer Science, Virtual University of Pakistan, Lahore, 55150, PakistanSchool of Computer Science, National College of Business Administration and Economics, Lahore, 54000, PakistanDepartment of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 12555, United Arab EmiratesFaculty of Pharmacy and Health Sciences, Department of Pharmacy, University of Balochistan, Quetta, 08770, PakistanSchool of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia; Corresponding author.Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi ArabiaThe 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.http://www.sciencedirect.com/science/article/pii/S2405844024004341Biomedical image processingComputer-aided diagnosisMedical image analysisMachine learningDeep learningHT-29 cells |
| spellingShingle | Inayatul Haq Tehseen Mazhar Rizwana Naz Asif Yazeed Yasin Ghadi Najib Ullah Muhammad Amir Khan Amal Al-Rasheed YOLO and residual network for colorectal cancer cell detection and counting Heliyon Biomedical image processing Computer-aided diagnosis Medical image analysis Machine learning Deep learning HT-29 cells |
| title | 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_short | YOLO and residual network for colorectal cancer cell detection and counting |
| title_sort | yolo and residual network for colorectal cancer cell detection and counting |
| topic | Biomedical image processing Computer-aided diagnosis Medical image analysis Machine learning Deep learning HT-29 cells |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024004341 |
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