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
    Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver
    ( 2022-11-01)
    Halim A.A.A.
    ;
    Allan Melvin Andrew
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Vijayasarveswari Veeraperumal
    ;
    Abd Rahman M.A.
    ;
    Zamin N.
    ;
    Mary M.R.
    ;
    Khatun S.
    Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
  • 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
    Erratum: Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction (PLoS ONE (2020) 15:8 (e0229367) DOI: 10.1371/journal.pone.0229367)
    ( 2021-05-01)
    Vijayasarveswari Veeraperumal
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    R Badlishah Ahmad
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    Sabira Khatun
    ;
    Hasliza A Rahim @ Samsuddin
    The authors are listed out of order. Please view the correct author order, affiliations, and citation here: V. Vijayasarveswari1, A.M. Andrew1, M. Jusoh1, R.B. Ahmad1, T. Sabapathy1, R.A.A. Raof1, M.N.M. Yasin1, S. Khatun2, H.A. Rahim1 1 Advanced Communication Engineering (ACE) Centre of Excellence, Universiti Malaysia Perlis, Kangar, Perlis, West Malaysia, 2 Faculty of Electrical & Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang Vijayasarveswari V, Andrew AM, Jusoh M, Ahmad RB, Sabapathy T, Raof RAA, et al. (2020) Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction. PLoS ONE 15(8): e0229367. https://doi.org/10.1371/journal.pone.0229367 There are errors in the Funding statement. The correct Funding statement is as follows: The study was supported by Fundamental Research Grant Scheme (FRGS), Ministry of Education Malaysia under grant number: FRGS/1/2019/TK04/UNIMAP/02/3. No additional external funding was received for this study.
      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