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YOLOv8 Outperforms Traditional CNN Models in Mammography Classification: Insights From a Multi‐Institutional Dataset

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cpe.5803openalex-snapshot

Classification of breast cancer mammogram images using convolution neural network

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Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

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Digital breast tomosynthesis improves diagnostic accuracy of breast microcalcifications

inferredcites →https://doi.org/10.1002/ima.22978openalex-snapshot

Multiloss strategy for breast cancer subtype classification using digital breast tomosynthesis

inferredcites →https://doi.org/10.1002/ima.23071openalex-snapshot

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

inferredcites →https://doi.org/10.1002/jum.15816openalex-snapshot

The Added Value of a Computer‐Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

inferredcites →https://doi.org/10.1007/978-3-030-33128-3_4openalex-snapshot

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram

inferredcites →https://doi.org/10.1007/s10278-023-00943-5openalex-snapshot

Robustness of Deep Networks for Mammography: Replication Across Public Datasets

inferredcites →https://doi.org/10.1016/j.bspc.2019.02.017openalex-snapshot

Benign and malignant classification of mammogram images based on deep learning

inferredcites →https://doi.org/10.1016/j.bspc.2021.102914openalex-snapshot

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

inferredcites →https://doi.org/10.1016/j.clinimag.2021.11.006openalex-snapshot

Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists

inferredcites →https://doi.org/10.1016/j.cmpb.2018.01.017openalex-snapshot

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

inferredcites →https://doi.org/10.1016/j.cmpb.2020.105584openalex-snapshot

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms

inferredcites →https://doi.org/10.1016/j.cmpb.2020.105823openalex-snapshot

YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms

inferredcites →https://doi.org/10.1016/j.cmpb.2022.106903openalex-snapshot

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

inferredcites →https://doi.org/10.1016/j.critrevonc.2020.103174openalex-snapshot

Breast cancer, screening and diagnostic tools: All you need to know

inferredcites →https://doi.org/10.1016/j.radi.2023.01.004openalex-snapshot

Identification of factors associated with diagnostic performance variation in reporting of mammograms: A review

inferredcites →https://doi.org/10.1038/s41591-020-01174-9openalex-snapshot

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

inferredcites →https://doi.org/10.1038/s41598-023-29875-4openalex-snapshot

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

inferredcites →https://doi.org/10.1109/csase51777.2022.9759702openalex-snapshot

Breast Cancer: Breast Tumor Detection Using Deep Transfer Learning Techniques in Mammogram Images

inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot

You Only Look Once: Unified, Real-Time Object Detection

inferredcites →https://doi.org/10.1148/radiol.231173openalex-snapshot

Mammography Breast Cancer Screening Triage Using Deep Learning: A UK Retrospective Study

inferredcites →https://doi.org/10.1155/2017/3640901openalex-snapshot

Three-Class Mammogram Classification Based on Descriptive CNN Features

inferredcites →https://doi.org/10.1177/0284185119858051openalex-snapshot

Validation of radiologists’ findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software

inferredcites →https://doi.org/10.1186/s12859-022-04965-8openalex-snapshot

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

inferredcites →https://doi.org/10.1371/journal.pone.0284605openalex-snapshot

Reliability of radiologists’ first impression when interpreting a screening mammogram

inferredcites →https://doi.org/10.19187/abc.20185398-105openalex-snapshot

Diagnostic Efficacy of Technetium-99m-Sestamibi Scintimammography in Comparison with Mammography to Detect Breast Lesions: A Systematic Review

inferredcites →https://doi.org/10.3390/app14072680openalex-snapshot

Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish

inferredcites →https://doi.org/10.3390/diagnostics11081409openalex-snapshot

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography

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