ArticleJournal of X-ray science and technology2017
Computer-aided classification of mammographic masses using visually sensitive image features.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.Tomography (Ann Arbor, Mich.) · 2022Article
- A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.Bioengineering (Basel, Switzerland) · 2022Article
- Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography.Frontiers in oncology · 2022Article
- Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.IEEE transactions on bio-medical engineering · 2021Article
- Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.Journal of biomedical informatics · 2021Article
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.IEEE transactions on medical imaging · 2020Article
- Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review.Journal of healthcare engineering · 2020Review
- 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
- Developing global image feature analysis models to predict cancer risk and prognosis.Visual computing for industry, biomedicine, and art · 2019Article
- Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.Annals of biomedical engineering · 2018Article
- Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker.Physics in medicine and biology · 2018Article
- Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.Physics in medicine and biology · 2018Article
- A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.Journal of X-ray science and technology · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
purposeTo develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms.
methodsAn image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score.
resultsAreas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025.
conclusionThis study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a "visual aid" interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features.
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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.