ArticleAsian Pacific journal of cancer prevention : APJCP2019
Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms.
Article in Asian Pacific journal of cancer prevention : APJCP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 19 citations in OpenAlex.
- Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.Diagnostics (Basel, Switzerland) · 2024Article
- Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- A Framework on Performance Analysis of Mathematical Model-Based Classifiers in Detection of Epileptic Seizure from EEG Signals with Efficient Feature Selection.Journal of healthcare engineering · 2022Article
- Classification of Homo sapiens gene behavior using linear discriminant analysis fused with minimum entropy mapping.Medical & biological engineering & computing · 2021Article
- Construction of a Potential Breast Cancer-Related miRNA-mRNA Regulatory Network.BioMed research international · 2020Article
- Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2019Article
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2 authors at 1 institution in 1 country.
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Abstract
Objective: Breast cancer is the most common invasive severity which leads to the second primary cause of death among women. The objective of this paper is to propose a computer-aided approach for the breast cancer classification from the digital mammograms. Methods: Designing an effective classification approach will assist in resolving the difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society (MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the Linear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results: Error rate, Sensitivity, Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used as a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion: The results of comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast cancer classification.
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