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Binary tunicate swarm algorithm based novel feature selection framework for mammographic mass classification
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DOI：10.1016/j.measurement.2024.114928.png)
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Abstract
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The efficacy of a CAD system for mammographic mass classification depends on high -quality features for maximum throughput. Feature selection is crucial for accurate breast cancer diagnosis in CAD solutions. Leveraging the success of metaheuristics, a novel binary tunicate swarm algorithm is proposed for feature selection. The framework incorporates pre -trained deep learning architectures as feature extractors, alongside the proposed feature selection and classifier, for mass categorization. The proposed feature selection strategy also emphasizes the design of new fitness functions tailored to the specific characterization of suspicious mass lesions. Experiments on benchmark databases (mini-MIAS and DDSM) yield sensitivities of 95.31% and 92.18%, false positives of 0.18 and 0.26 per image, and diagnosis accuracies of 97.86% and 93.86%, and in three -class categorization, F 1 scores for malignancy reach 0.93 and 0.84, respectively. Overall, the results demonstrate promising progress compared to competing schemes in metaheuristics-based feature selection within the state-of-the-art.
Keywords:
CAD system
Binary tunicate swarm algorithm
Feature selection
Mammographic mass categorization
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