- 3.3Impact Factor
- 6.7CiteScore
- 16 daysTime to First Decision
Journal of Imaging, Volume 5, Issue 9
September 2019 - 5 articles
Cover Story: Machine learning has been used to assist radiologists in making accurate decisions to diagnose and classify breast cancer at an early stage. We introduced classification models based on stack generalization that can help radiologists to predict whether microcalcification (MC) clusters are benign or malignant. Clinically grounded features of MC clusters were extracted from the segmented images. We provided an insight into how different meta-classifiers associated with stack-generalization models can improve the classification accuracy. The best classification accuracy was reported for the Optimam Mammography Image Database (OMI-DB) with a classification accuracy of around 95.00 ± 0.57 %. View this paper
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J. Imaging - ISSN 2313-433X