Cargando…

Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning

Mammography plays an important role in screening breast cancer among females, and artificial intelligence has enabled the automated detection of diseases on medical images. This study aimed to develop a deep learning model detecting breast cancer in digital mammograms of various densities and to eva...

Descripción completa

Detalles Bibliográficos
Autores principales:	Suh, Yong Joon, Jung, Jaewon, Cho, Bum-Joo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2020
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711783/ https://www.ncbi.nlm.nih.gov/pubmed/33172076 http://dx.doi.org/10.3390/jpm10040211

Ejemplares similares

Automated histological classification for digital pathology images of colonoscopy specimen via deep learning
por: Byeon, Sun-ju, et al.
Publicado: (2022)

Density is in the eye of the beholder: visual versus semi-automated assessment of breast density on standard mammograms
por: Lobbes, M. B. I., et al.
Publicado: (2011)

Intelligent Breast Mass Classification Approach Using Archimedes Optimization Algorithm with Deep Learning on Digital Mammograms
por: Basheri, Mohammed
Publicado: (2023)

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms
por: Abdelsamea, Mohammed M, et al.
Publicado: (2019)

Detecting and classifying lesions in mammograms with Deep Learning
por: Ribli, Dezső, et al.
Publicado: (2018)

Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms
por: Cai, Hongmin, et al.
Publicado: (2019)

Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study
por: Gubern-Mérida, Albert, et al.
Publicado: (2014)

Case-control study of mammographic density and breast cancer risk using processed digital mammograms
por: Habel, Laurel A., et al.
Publicado: (2016)

Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms
por: Tzortzis, Ioannis N., et al.
Publicado: (2022)

Automated Feature Set Selection and Its Application to MCC Identification in Digital Mammograms for Breast Cancer Detection
por: Huang, Yi-Jhe, et al.
Publicado: (2013)

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
por: Garrucho, Lidia, et al.
Publicado: (2023)

Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study
por: Tsochatzidis, Lazaros, et al.
Publicado: (2019)

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach
por: Mobini, Nazanin, et al.
Publicado: (2023)

Impact of image compression on deep learning-based mammogram classification
por: Jo, Yong-Yeon, et al.
Publicado: (2021)

Area and Volumetric Density Estimation in Processed Full-Field Digital Mammograms for Risk Assessment of Breast Cancer
por: Cheddad, Abbas, et al.
Publicado: (2014)

Study of the Effect of Breast Tissue Density on Detection of Masses in Mammograms
por: García-Manso, A., et al.
Publicado: (2013)

Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology
por: Wang, Hongyu, et al.
Publicado: (2018)

Generating Full-Field Digital Mammogram From Digitized Screen-Film Mammogram for Breast Cancer Screening With High-Resolution Generative Adversarial Network
por: Zhou, Yuanpin, et al.
Publicado: (2022)

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms
por: Samee, Nagwan Abdel, et al.
Publicado: (2022)

Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning
por: Gudhe, Naga Raju, et al.
Publicado: (2022)

Local breast density at lesion sites in diagnostic and previous screening mammograms
por: Otsuka, M, et al.
Publicado: (2014)

Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms
por: Zyout, Imad, et al.
Publicado: (2009)

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning
por: Kim, Young Jae, et al.
Publicado: (2022)

PB.23. Breast density in previous screening mammograms of women with and without breast cancer
por: Sivagnanam, L, et al.
Publicado: (2014)

Fuzzy technique for microcalcifications clustering in digital mammograms
por: Vivona, Letizia, et al.
Publicado: (2014)

Primary breast osteosarcoma mimicking calcified fibroadenoma on screening digital breast tomosynthesis mammogram
por: Bennett, Debbie Lee, et al.
Publicado: (2017)

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning
por: Cho, Bum-Joo, et al.
Publicado: (2022)

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis
por: Razali, Noor Fadzilah, et al.
Publicado: (2023)

Fully Automated Breast Density Segmentation and Classification Using Deep Learning
por: Saffari, Nasibeh, et al.
Publicado: (2020)

Automated Classification of Colorectal Neoplasms in White-Light Colonoscopy Images via Deep Learning
por: Yang, Young Joo, et al.
Publicado: (2020)

Kinetic patterns of benign and malignant breast lesions on contrast enhanced digital mammogram
por: Huang, Jer-Shyung, et al.
Publicado: (2020)

The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison
por: North, Frederick, et al.
Publicado: (2021)

Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset
por: Adedigba, Adeyinka P., et al.
Publicado: (2022)

Performance in digital mammography with and without film prior mammograms
por: Taylor-Phillips, S, et al.
Publicado: (2009)

Mammographic density defined by higher than conventional brightness threshold better predicts breast cancer risk for full-field digital mammograms
por: Nguyen, Tuong Linh, et al.
Publicado: (2015)

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network
por: P, Shenbagavalli, et al.
Publicado: (2018)

Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms
Publicado: (2022)

Perceived Sufficiency of Full-Field Digital Mammograms With and Without Irreversible Image Data Compression for Comparison with Next-Year Mammograms
por: Destounis, Stamatia, et al.
Publicado: (2010)

Correlation of Breast Density Grade on Mammogram With Diagnosed Breast Cancer: A Retrospective Cross-Sectional Study
por: Sajjad, Barka, et al.
Publicado: (2022)

Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms
por: Pollán, Marina, et al.
Publicado: (2013)

Cannot write session to /tmp/vufind_sessions/sess_0mb6veiv7oei3ilbbhvif14dej