ItemConference Paper

An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine

Title
An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine
Creator
Singh, Davinder Paul; Rani, Rozy; Sindhu, V.; Revathi, V.M.; Kosalairaman, T.; Paul, P. Mano
Description
The long-term prognosis and mortality rates can be improved with early identification of breast cancer. The time-consuming and expensive procedures of mammography, MRI, ultrasound, CT, PT, and biopsy have been the subject of much research; nevertheless, these approaches are not suitable for younger women and can be rather expensive. This study employed cutting-edge image processing to improve early breast cancer detection. The researchers utilised anisotropic filtering to reduce background noise in medical images after picking mammograms at random from the Digital Database for Screening Mammography. The use of morphology-based feature extraction allowed for autonomous and accurate categorisation after mass segmentation using a genetic algorithm with recurrent thresholding. By merging a KF with an ELM enhanced with an AV, a new model named KF-av-elm improves diagnostic accuracy. Medical imaging noise and estimating errors are both significantly reduced by the combination method. Their accuracy rating of 98.28% allowed them to outperform other approaches. The KF-av-elm model appears to be a reliable, efficient, and effective diagnostic tool; its adoption may lead to better identification and outcomes for breast cancer patients. 2025 IEEE.
Source
2025 Global Conference on Information Technology and Communication Networks, GITCON 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
African Vultures Optimization Algorithm (AVOA); Breast Cancer (BC); Deoxyribonucleic Acid (DNA); Genetic Algorithm (GA); Kalman filter (KF)
Coverage
Singh D.P., Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gujarat, Gandhinagar, India; Rani R., Applied Sciences, Chandigarh Group of Colleges Jhanjeri, Chandigarh Engineering College, Punjab, Mohali, India; Sindhu V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Revathi V.M., Department of Mathematics, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India; Kosalairaman T., Department of Information Technology, V. S. B. Engineering College, Tamilnadu, Karur, India; Paul P.M., Department of CSE, Artificial Intelligence, Dayananda Sagar Academy of Technology and Management, Karnataka, Udayapura, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833153348-9;
Format
online
Language
English
Type
Conference paper

Citation

Singh, Davinder Paul; Rani, Rozy; Sindhu, V.; Revathi, V.M.; Kosalairaman, T.; Paul, P. Mano, “An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25848.

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