ReviewTechnology in cancer research & treatment
Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis.
Review in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Reciprocal cooperative gating fusion of SqueezeNet and ShuffleNetV2 for breast cancer detection in histopathology images.Scientific reports · 2026Article
- Research progress in computer-aided diagnosis systems for lung cancer.NPJ digital medicine · 2025Review
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types.Journal of imaging informatics in medicine · 2025Article
- Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- Enhancing cancer risk awareness and screening management through artificial intelligence: a narrative review.Frontiers in oncology · 2025Review
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.Radiology. Imaging cancer · 2024Article
- Breast cancer screening in women taking hormone replacement therapy needs updating.Facts, views & vision in ObGyn · 2024Article
- Challenges and Innovations in Breast Cancer Screening in India: A Review of Epidemiological Trends and Diagnostic Strategies.International journal of breast cancer · 2024Review
- The application of traditional machine learning and deep learning techniques in mammography: a review.Frontiers in oncology · 2023Review
- 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.
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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.