ArticleCancers2021
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Deep Learning Based on Automated Breast Volume Scanner Images for the Diagnosis of Breast Lesions: A Multicenter Diagnostic Study.International journal of medical sciences · 2025Trial
- Deep learning in breast cancer histopathology: predicting and detecting axillary lymph node metastasis.Virchows Archiv : an international journal of pathology · 2026Review
- MRNet: A Multi-Resolution Dual-Task Framework for Micrometastases Detection in Breast Cancer Sentinel Lymph Nodes.Journal of imaging informatics in medicine · 2026Article
- AdaptiveInvolutionNet: Spatially-adaptive involution with channel-wise attention for breast MRI tumor classification.PloS one · 2026Article
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
- Breast cancer detection based on histological images using fusion of diffusion model outputs.Scientific reports · 2025Article
- Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody-Associated Glomerulonephritis.Kidney international reports · 2025Article
- Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks.Frontiers in medicine · 2025Article
- Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.Frontiers in microbiology · 2025Article
- AI-augmented pathology: the experience of transfer learning and intra-domain data diversity in breast cancer metastasis detection.Frontiers in oncology · 2025Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
- A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.PloS one · 2024Article
- ANFIS Fuzzy convolutional neural network model for leaf disease detection.Frontiers in plant science · 2024Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Data mining techniques in breast cancer diagnosis at the cellular-molecular level.Journal of cancer research and clinical oncology · 2023Article
- Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.Scientific reports · 2023Article
- The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images' center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet.
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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.