ReviewEXCLI journal2017
Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.
Review in EXCLI journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 115 citations in OpenAlex.
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Semiautomated segmentation of breast tumor on automatic breast ultrasound image using a large-scale model with customized modules.Scientific reports · 2025Article
- HER-2-Targeted Electrochemical Sensors for Breast Cancer Diagnosis: Basic Principles, Recent Advancements, and Challenges.Biosensors · 2025Review
- Article
- An approach for classification of breast cancer using lightweight deep convolution neural network.Heliyon · 2024Article
- A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer.Life (Basel, Switzerland) · 2023Review
- Vaccines in Breast Cancer: Challenges and Breakthroughs.Diagnostics (Basel, Switzerland) · 2023Review
- Skin Cancer Detection Using Deep Learning-A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing.Journal of clinical medicine · 2023Article
- The utilization of artificial intelligence applications to improve breast cancer detection and prognosis.Saudi medical journal · 2023Review
- Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3.Sensors (Basel, Switzerland) · 2022Article
- The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.European radiology · 2022Article
- Article
- Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.Cancers · 2022Article
- Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis.Frontiers in oncology · 2022Article
- The diagnostic value and safety of modified needle aspiration biopsy for superficial lymphadenectasis.Frontiers in surgery · 2022Article
- A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis.Open life sciences · 2022Review
- Connected-UNets: a deep learning architecture for breast mass segmentation.NPJ breast cancer · 2021Article
- [Peripheral bloodNan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks.Journal of digital imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.