ArticleDiagnostics (Basel, Switzerland)2023
A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Deep meta-learning framework with U-Net segmentation and optimization-based feature selection for breast cancer detection in MRI images.Quantitative imaging in medicine and surgery · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.Scientific reports · 2026Article
- Enhanced breast cancer detection framework based on YOLOv11n with multi-scale feature calibration.Scientific reports · 2026Article
- Deep transfer learning for breast cancer detection in underserved regions.Frontiers in oncology · 2026Article
- Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models.Sensors (Basel, Switzerland) · 2025Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Development of a clinical decision support system for breast cancer detection using ensemble deep learning.Scientific reports · 2025Article
- Breast Cancer Classification with Various Optimized Deep Learning Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Employing transfer learning for breast cancer detection using deep learning models.PLOS digital health · 2025Article
- Breast cancer classification based on hybrid CNN with LSTM model.Scientific reports · 2025Article
- LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Research trends on AI in breast cancer diagnosis, and treatment over two decades.Discover oncology · 2024Review
- XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.Scientific reports · 2024Article
- Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.Journal of cancer research and clinical oncology · 2023Review
- A Hybrid CNN-Transformer Deep Learning Model for Differentiating Benign and Malignant Breast Tumors Using Multi-View Ultrasound Images.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community's interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model's superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively.
Indexed as
Identifiers
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.