ArticleJournal of imaging2022
YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Real-time AI-guided ultrasound localization method for breast tumor rotational resection.Frontiers in oncology · 2025Article
- Detection of Aortic Dissection and Intramural Hematoma in Non-Contrast Chest Computed Tomography Using a You Only Look Once-Based Deep Learning Model.Journal of clinical medicine · 2024Article
- Presegmenter Cascaded Framework for Mammogram Mass Segmentation.International journal of biomedical imaging · 2024Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking.Sensors (Basel, Switzerland) · 2023Article
- TRUST: A Novel Framework for Vehicle Trajectory Recovery from Urban-Scale Videos.Sensors (Basel, Switzerland) · 2022Article
- Image Augmentation Techniques for Mammogram Analysis.Journal of imaging · 2022Review
- Elimination of Defects in Mammograms Caused by a Malfunction of the Device Matrix.Journal of imaging · 2022Article
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Authors and funding
6 authors.
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Abstract
backgroundWe directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model.
methodWe used 1080 images to train the YOLOv4, plus 100 images with proven breast cancer (BC) and 100 images with proven absence of BC to test both models.
resultsthe rates of true-positive, false-positive and false-negative outcomes were 60, 10 and 40, respectively, for YOLOv4, and 93, 63 and 7, respectively, for NCA. The sensitivities for the YOLOv4 and the NCA were comparable to each other for star-like lesions, masses with unclear borders, round- or oval-shaped masses with clear borders and partly visualized masses. On the contrary, the NCA was superior to the YOLOv4 in the case of asymmetric density and of changes invisible on the dense parenchyma background. Radiologists changed their earlier decisions in six cases per 100 for NCA. YOLOv4 outputs did not influence the radiologists' decisions.
conclusionsin our set, NCA clinically significantly surpasses YOLOv4.
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