Vijayasarveswari Veeraperumal
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Vijayasarveswari Veeraperumal
Official Name
Veeraperumal, Vijayasarveswari
Alternative Name
VEERAPERUMAL, V.
Main Affiliation
Scopus Author ID
57226571921
Researcher ID
EDU-3323-2022

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  • Publication
    Existing and emerging breast cancer detection technologies and its challenges: A review
    ( 2021-11-01)
    Ahmad Ashraf Abdul Halim
    ;
    Allan Melvin Andrew
    ;
    Mohd Najib Mohd Yasin
    ;
    Abd Rahman M.A.
    ;
    Muzammil Jusoh
    ;
    Vijayasarveswari Veeraperumal
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Illahi U.
    ;
    Abdul Karim M.K.
    ;
    Scavino E.
    Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.
      1
  • Publication
    UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set
    ( 2023-01-01)
    Ahmad Ashraf Abdul Halim
    ;
    Vijayasarveswari Veeraperumal
    ;
    Allan Melvin Andrew
    ;
    Mohd Najib Mohd Yasin
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Hossain K.
    ;
    Bari B.S.
    ;
    Fatinnabila Kamal
    Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.
      1
  • Publication
    Development of statistically modelled feature selection method for microwave breast cancer detection
    (Semarak Ilmu Publishing, 2025)
    Vijayasarveswari Veeraperumal
    ;
    Norfadila Mahrom
    ;
    Rafikha Aliana A Raof
    ;
    Phak Len Al Eh Kan
    ;
    Muhammad Amiruddin Ab Razak
    ;
    Bavanraj Punniya Silan
    ;
    Yusnita Rahayu
    ;
    Mohd Wafi Nasrudin
    ;
    Ahmad Ashraf Abdul Halim
    ;
    Nuraminah Ramli
    Microwave technology is very promising tool for breast cancer detection. Microwave transmits and receives UWB signals. UWB signals carries information of the breast cancer. UWB signals need to be pre-processed in order to remove irrelevant and redundant features. Feature extraction and feature selection methods are mostly used to remove the unwanted features. In this paper, a statistically modelled feature selection (SMFS) method is proposed for microwave breast cancer detection. Initially, performance of different feature extraction and feature selection method are analysed using Anova test (p-value) and machine learning (SVM, DT, PNN, NB) accuracy. The best feature extraction and feature selection methods are combined and tested. Based on the performance of feature extraction and feature selection method, Combined Neighbour Component Analysis (feature selection) and Statistical features (feature extraction) are combined and tested. This method is able to achieve up to 85%. The result proves two stage methods are able to improve the accuracy compared to single stage method. Therefore, SMFS is able to detect breast cancer efficiently.
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