ReviewJournal of healthcare engineering2020
Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review.
Review in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
32 citing papers in PubMed, 116 citations in OpenAlex.
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Detection and Classification of Lesions in Mammograms using One-Stage Models.Journal of biomedical physics & engineering · 2025Article
- From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.Diagnostics (Basel, Switzerland) · 2025Review
- Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Article
- Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging.Cancers · 2024Article
- CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks.Heliyon · 2024Article
- Deep learning approaches for breast cancer detection in histopathology images: A review.Cancer biomarkers : section A of Disease markers · 2024Review
- Challenges and Innovations in Breast Cancer Screening in India: A Review of Epidemiological Trends and Diagnostic Strategies.International journal of breast cancer · 2024Review
- A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer.Life (Basel, Switzerland) · 2023Review
- Article
- Vaccines in Breast Cancer: Challenges and Breakthroughs.Diagnostics (Basel, Switzerland) · 2023Review
- Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization.Biomimetics (Basel, Switzerland) · 2023Article
- Article
- BCDiagnostics (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the American Cancer Society's forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.
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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.