ArticleJournal of X-ray science and technology2017
A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.
Article in Journal of X-ray science and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 105 citations in OpenAlex.
- MobNas ensembled model for breast cancer prediction.Scientific reports · 2025Article
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.PloS one · 2024Article
- Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography.Healthcare (Basel, Switzerland) · 2022Article
- Deep Learning Hybrid Techniques for Brain Tumor Segmentation.Sensors (Basel, Switzerland) · 2022Article
- A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram.Scientific reports · 2022Article
- Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics.Insights into imaging · 2022Article
- COVID-19 diagnosis system by deep learning approaches.Expert systems · 2022Article
- Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.Physics in medicine and biology · 2022Article
- Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Frontiers in oncology · 2022Review
- Review
- Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.Biosensors · 2021Article
- Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.IEEE transactions on bio-medical engineering · 2021Article
- Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.AJR. American journal of roentgenology · 2020Article
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.IEEE transactions on medical imaging · 2020Article
- Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.Computer methods and programs in biomedicine · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeTo develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses.
methodsAn image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network.
resultsThe results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019.
conclusionsThis study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.
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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.