COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY
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Summary
This paper focuses on the classification of segmented local bright spots as either calcification or noncalcification in mammographic images and seven classifiers (linear and quadratic classifiers, binary decision trees, standard backpropagation network, 2 dynamic neural networks, and a K-nearest neighbor) are compared.
- Type
- article
- Published
- 1993-12-01
- Cited by
- 210
- References
- 17
- OpenAlex
- https://openalex.org/W2065330407
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:62604959
Keywords
Artificial intelligence, Pattern recognition (psychology), Computer science, Mammography, Segmentation
References
- Computer and Robot Vision
- Automatic computer detection of clustered calcifications in digital mammograms
- Evaluation of mammographic calcifications using a computer program.
- Connectionist ideas and algorithms
- Algorithm for the detection of fine clustered calcifications on film mammograms.
- Mammogram Inspection by Computer
- Breast calcifications: analysis of imaging properties.
- Some practical issues of experimental design and data analysis in radiological ROC studies.
- Stochastic model for automated detection of calcifications in digital mammograms
- Pattern classification and scene analysis
- An iterative growing and pruning algorithm for classification tree design
- A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
- The Cascade-Correlation Learning Architecture
- Segmentation of microcalcifications in mammograms
- The meaning and use of the area under a receiver operating characteristic (ROC) curve.
- Shape analysis of mammographic calcifications
- Computer-aided detection of microcalcifications in mammograms. Methodology and preliminary clinical study.
Cited by
- Détection automatique des opacités en tomosynthèse numérique du sein. (Automatic detection of masses in digital breast tomosynthesis datasets)
- Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform
- Imbalanced Training Set Reduction and Feature Selection Through Genetic Optimization
- Computer-aided detection for digital breast tomosynthesis
- Decision trees and integrated features for computer aided mammographic screening
- Applying the wrapper approach for auto discovery of under-sampling and over-sampling percentages on skewed datasets
- Effect of training set selection when predicting defaulter SMEs with unbalanced data
- Automatic recognition of baby gesture
- Anomaly detection based on zero appearances in subspaces
- Evolutionary data analysis for the class imbalance problem
- Breast Cancer Treatment Evaluation based on Mammographic and Echographic Distance Computing
- Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
- A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications
- Higher accuracy and throughput in computer-aided screening of mammographic microcalcifications
- Microcalcification evaluation in computer assisted diagnosis for digital mammography
- The detection of micro-calcifications in mammographic images using high dimensional features
- Bi-modal breast cancer classification system
- Advances in prokaryote classification from microscopic images
- Handling Class Overlap and Imbalance to Detect Prompt Situations in Smart Homes
- A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms
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