ArticleJournal of imaging2019
Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.
Article in Journal of imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications.European radiology experimental · 2021Article
- OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data.Radiology. Artificial intelligence · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 .
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Registered trials
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