ArticleJournal of medical systems2012
An expert support system for breast cancer diagnosis using color wavelet features.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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
6 citing papers in PubMed, 40 citations in OpenAlex.
- A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.Journal of medical systems · 2019Article
- Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.EXCLI journal · 2017Review
- Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.Journal of medical systems · 2014Review
- Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification.Journal of medical systems · 2014Article
- Article
- Comparison of SVM and ANFIS for snore related sounds classification by using the largest Lyapunov exponent and entropy.Computational and mathematical methods in medicine · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.