Nordiana Shariffudin
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Nordiana Shariffudin
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Nordiana, Shariffudin
Alternative Name
Shariffudin, N.
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Scopus Author ID
36968747300

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  • Publication
    Automatic Recognition System of Iron Deficiency Anaemia in Human RBC using Machine Learning Techniques
    ( 2023-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Ibrahim W.N.A.B.W.
    ;
    Nordin S.A.
    ;
    Tohit E.R.B.M.
    ;
    Ali H.B.
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Iron Deficiency Anaemia (IDA) is the most common blood disorder. According to WHO, 30% of women aged 15-49 years, 37% of pregnant women, as well as 40% of children aged 6-59 months are anaemic globally. Anaemia can cause premature birth and affect mental, physical, and cognitive development, which in turn will lead to birth weight problems and stunted birth. The process of detecting IDA is usually captured based on a thin blood smear utilizing microscopic observation. Nevertheless, this process can be time-consuming. Moreover, it is challenging to identify the difference between IDA and normal red blood cells (RBCs) because the size is similar based on the observation of the human eye. It will cause difficulty in giving drug treatment to patients. A computeraided diagnosis (CAD) method was created to automatically distinguish between IDA and normal RBCs. The processes started with image acquisition, image processing, and recognition. Additionally, a Graphical User Interface (GUI) is also used to display images. In conclusion, recognition was done using the Multilayer Perceptron (MLP) method. The findings indicate that the proposed automated system is effective at distinguishing between IDA as well as normal RBCs, having an accuracy of 97.58% with regard to training and 98% regarding validation utilizing Levenberg-Marquardt (LM) trained MLP.
  • Publication
    Intelligent Classification Procedure for Plasmodium Knowlesi Malaria Species
    ( 2022-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Yusoff Mashor
    ;
    Mohamed Z.
    ;
    Jusman Y.
    ;
    Hasimah Ali
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Plasmodium knowlesi (PK) is the fifth most prevalent malarial parasite species that causes serious health problems. Generally, PK present in a thin blood smear is observed using a microscope to differentiate between trophozoites (PKT), schizonts (PKS), gametocytes (PKG), and white blood cells (WBCs). This process is time-consuming and strenuous for the human eye. This study developed an intelligent classification procedure for PK using image processing and classification methods. The processes involved starting from image acquisition, and contrast enhancement based on Combination Local and Global Statistical Data (CLGSD), and local contrast stretching (LCS). Subsequently, a segmentation procedure was developed to segment the malaria images into two regions, namely malarial parasites and background regions. The proposed 16 feature sets were extracted, which consisted of the size of the object, size ratio of the object per infected RBC, and seven moments for each object shape based on size and perimeter. Finally, to validate the procedure performance, the proposed procedure was tested using 800 malarial parasites and WBC images. The results showed that the proposed procedure can classify three stages of PK, namely PKT, PKS, and PKG, as well as WBCs with an accuracy of 99.56% for training and 98.84% for validation, using a multi-layer perceptron (MLP) trained using the Levernberg-Marquardt (LM) algorithm.
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