Sridevi, V (2024) Breast Cancer Examination in Digitized MammogramsusingIntegrated K-Means Clustering with Garbor Filter and Shrunk Kernel KNNMethod. Breast Cancer Examination in Digitized MammogramsusingIntegrated K-Means Clustering with Garbor Filter and Shrunk Kernel KNNMethod, 17 (23). pp. 2444-2454. ISSN 0974-6846

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Abstract

Objectives: To suggest an intelligent classification system for efficient breast
cancer diagnosis that distinguishes between benign and malignant breast
cancer. The goal of the research is to develop a unique CAD system for the
detection & classification of breast cancer using novel K-Means clustering
(KMC) with Gabor Filter (GF) and Shrunk Kernel K-Nearest Neighbor (KNN)
classifier. Methods: Two different sorts of perspectives, such as Craniocaudal
(CC) and Mediolateral oblique (MLO) mammograms are employed to improve
diagnostic effectiveness. Utilizing an adaptive K-means clustering technique to
segment the tumor. The Gabor filter is used in conjunction with the k-means
clustering method to extract the features of the CC and MLO perspectives.
The mammography image is finally classified into benign and malignant using
a unique Shrunk Kernel K-Nearest Neighbor (SKKNN) classifier. The biopsy
proven annotated mammogramsfromtheCBIS-DDSMdatasetareusedinthis
study. There were 6156 occurrences in the dataset with MLO and CC view of
1331 normal, 858 benign and 889 malignant mammograms. Findings: The
experimental findings showed that the suggested model KMC-GF and SKKNN
can accurately detect breast cancer at an early stage. The accuracy, sensitivity ,
specificity, AUC, precision, F1-measure for SKKNN was 92.56%, 93.8%, 92.75%,
95.2%, 93.93%, and 94.5% which are higher comparing single view features

Item Type: Article
Additional Information: SKKNN, Adaptive K-means segmentation, Gabor filter, MLO and CC Mammogram, KMC-GF
Uncontrolled Keywords: SKKNN; Adaptive K-means segmentation; Gabor filter; MLO and CC Mammogram;KMC-GF
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 06 Oct 2025 06:11
Last Modified: 06 Oct 2025 06:11
URI: https://ir.psgcas.ac.in/id/eprint/2454

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