Rafikha Aliana A Raof
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Rafikha Aliana A Raof
Official Name
Rafikha Aliana, A Raof
Alternative Name
Raof, Rafikha Aliana A.
Raof, R. A.A.
Raof, Rafikha Alaina A.
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Scopus Author ID
57075005500

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  • Publication
    Reliable Early Breast Cancer Detection using Artificial Neural Network for Small Data Set
    ( 2021-03-01)
    Vijayasarveswari V.
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Khatun S.
    ;
    Iszaidy I.
    This paper proposes a breast cancer detection module using Artificial Neural Network for small data set. The developed system consists of hardware and software. Hardware included UWB transceiver and a pair of home-made directional sensor/antenna. The software included a Graphical User Interface (GUI) and k-fold based feed-forward back propagation Neural Network module to detect the tumor existence, size and location along with soft interface between software and hardware. Forward scattering technique is used by placing two sensors diagonally opposite sides of a breast phantom. UWB pulses are transmitted from one side of phantom and received from other side, controlled by the software interface in PC environment. Firstly feed forward backpropagation neural network (FFBNN) is developed. Then, k-fold is combined with developed FFBNN for testing purpose. Four data sets are created where contains 125, 95, 65 and 30 data samples in 1st,2nd,3rd and 4th data set respectively. Collected received signals were then fed into the NN module for training, testing and validation. The process is done for all data sets separately. The system exhibits detection efficiency of tumor existence, location (x, y, z), and size were approximately 87.72%, 87.24%, 83.93% and 80.51% for 1st, 2nd, 3rd and 4th data set respectively. The proposed module is very practical with low-cost and user friendly. The developed breast cancer detection module can be used for large data samples as well as for minimum data samples.
  • Publication
    Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
    ( 2020-08-01)
    Vijayasarveswari V.
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    R Badlishah Ahmad
    ;
    Khatun S.
    ;
    Hasliza A Rahim @ Samsuddin
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multistage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
      2  39
  • 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  31