ArticleJournal of medical physics
Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and
Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients.Journal of medical physicsArticle
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2 authors.
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Abstract
Introduction: Although positron emission tomography/computed tomography (PET/CT) is a common tool for measuring breast cancer (BC), subtypes are not automatically classified by it. Therefore, the purpose of this research is to use an artificial neural network (ANN) to evaluate the clinical subtypes of BC based on the value of the tumor marker. Materials and Methods: In our nuclear medical facility, 122 BC patients (training and testing) had Results: With an accuracy of 95.77%, the result illustrates the ANN model for K-fold cross-validation. The mean values of specificity and sensitivity were 0.955 and 0.958, respectively. The area under the curve on average was 0.985. Conclusion: Subtypes of BC may be categorized using the suggested approach. The PET/CT may be updated to diagnose BC subtypes using the appropriate tumor maker value when the suggested model is clinically implemented.
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