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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14

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Published Paper Details
Paper Title: Genetic Algorithm Based Feature Selection for the Classification of Breast Masses in Mammograms
Authors Name: Rakesh Prasanna R , V.Aravinda Rajan
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IJRTI_185816
Published Paper Id: IJRTI2304035
Published In: Volume 8 Issue 4, April-2023
DOI:
Abstract: In this paper, a 3-class classification system is proposed to classify the breast masses in mammogram images into benign, malignant and normal. The proposed algorithm consists of five modules: pre-processing of images, segmentation of Region of Interest (RoI), feature extraction, feature selection and classification. Pre-processing of an image is done by using Adaptive Histogram Equalization (AHE). Region growing algorithm is used to segment the Region of Interest (ROI) from the pre-processed image. Feature matrix is generated by using Gray Level Co-occurrence Matrix (GLCM) for the segmented ROI. A Genetic algorithm (GA) is used for selecting an optimal set of features from the feature matrix. The performance of GA is compared with a t-test. To evaluate the performance, three classifiers are used and the results are compared. They are multiSVM, KNN, and Naive Bayes classifiers. Totally, six combinations are evaluated. GA+multiSVM, GA+KNN, GA+Naive Bayes, t-test+multiSVM, t-test+KNN, and t-test+Naive Bayes. It is observed that GA+KNN combination outperforms with respect to accuracy. The mammogram images used in this work are collected from the publicly available dataset, Digital Database for Screening Mammography (DDSM).
Keywords: -Mammogram, Pre-processing, Genetic Algorithm, t-test, KNN, multiSVM, NaiveBayes
Cite Article: "Genetic Algorithm Based Feature Selection for the Classification of Breast Masses in Mammograms", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.209 - 219, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304035.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2304035
Registration ID:185816
Published In: Volume 8 Issue 4, April-2023
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Page No: 209 - 219
Country: madurai, Tamil Nadu, India
Research Area: Other
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2304035
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2304035
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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