ArticleHeliyon2024
YOLO and residual network for colorectal cancer cell detection and counting.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images.Discover oncology · 2026Article
- Automated Tumor Segmentation in Breast-Conserving Surgery Using Deep Learning on Breast Tomosynthesis.Journal of imaging informatics in medicine · 2025Article
- Image Sensing for Motorcycle Active Safety Warning System: Using YOLO and Heuristic Weighting Mechanism.Sensors (Basel, Switzerland) · 2025Article
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
- CA-YOLO: An Efficient YOLO-Based Algorithm with Context-Awareness and Attention Mechanism for Clue Cell Detection in Fluorescence Microscopy Images.Sensors (Basel, Switzerland) · 2025Article
- An effective study on the diagnosis of colon cancer with the developed local binary pattern method.Scientific reports · 2025Article
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- IEGS-BoT: An Integrated Detection-Tracking Framework for Cellular Dynamics Analysis in Medical Imaging.Biomimetics (Basel, Switzerland) · 2025Article
- Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks.Discover oncology · 2025Article
- Reducing M2 macrophage in lung fibrosis by controlling anti-M1 agent.Scientific reports · 2025Article
- A lightweight CNN for colon cancer tissue classification and visualization.Frontiers in oncology · 2025Article
- Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm.MethodsX · 2024Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
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7 authors.
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Abstract
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.
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Registered trials
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