Norfadila Mahrom
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Preferred name
Norfadila Mahrom
Official Name
Norfadila, Mahrom
Alternative Name
Mahrom, Norfadila
Mahrom, N.
Mahrom, Norfadilla
Main Affiliation
Scopus Author ID
57200080959
Researcher ID
DDY-3868-2022

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  • Publication
    Enhancement and segmentation of Ziehl Neelson sputum slide images using contrast enhancement and Otsu threshold technique
    (Semarak Ilmu Publishing, 2023)
    Ainul Kamilah Mohd Yusoff
    ;
    Rafikha Aliana A Raof
    ;
    Norfadila Mahrom
    ;
    Siti Suraya Md Noor
    ;
    Fazrul Faiz Zakaria
    ;
    Phak Len Al Eh Kan
    Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.
  • 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.
      1  4
  • Publication
    Tuberculosis Classification Using Deep Learning and FPGA Inferencing
    ( 2023-02-01)
    Fazrul Faiz Zakaria
    ;
    Asral Bahari Jambek
    ;
    Norfadila Mahrom
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Nazri Mohd Warip
    ;
    Al Eh Kan P.L.
    ;
    Mustapa M.
    Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.
      2