Wan Azani Wan Mustafa
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Preferred name
Wan Azani Wan Mustafa
Official Name
Wan Azani, Wan Mustafa
Alternative Name
Mustafa, W.
Azani Mustafa, Wan
Mustaffa, Wan Azani
Wan Mustafa, Wan Azani
Main Affiliation
Scopus Author ID
57219421621
Researcher ID
J-4603-2014

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  • Publication
    Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images
    ( 2023-01-01)
    Badarneh A.
    ;
    Alzuet A.
    ;
    Wan Azani Wan Mustafa
    ;
    Alquran H.
    ;
    Alsalatie M.
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells in whole slide images automatically by employing various image techniques. The proposed approach consists of four main steps; image enhancement, transform the extended minima, remove small pixels, and count the number of abnormal cells in the image. The proposed system used 250 cervical pap smear images where the overlap between cells is minimal. The performance of the proposed system is evaluated based on comparing the manual counting and automating counting over whole images. Therefore, the accuracy is evaluated mainly on the difference between manual and automated, and it is 92.5%. The proposed method can be used in laboratory to decrease the false positive rates in counting abnormal cells.
  • Publication
    Image Dataset for Cervical Cell Diagnosis - a Review
    ( 2023-01-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Ghani M.M.
    ;
    Hanafi H.F.
    ;
    Lah N.H.C.
    ;
    Ismail S.
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Thus, as to encourage talented researchers in this field, an excellent, easily access and expert's validated data for cervical cell has been developed by previous researchers. In this study, datasets have been reviewed from previous studies that can be access for research and study purposes.
      1
  • Publication
    Nucleus Detection Using Deep Learning Approach on Pap Smear Images
    ( 2023)
    Alquran Hiam
    ;
    Wan Azani Wan Mustafa
    ;
    Mohammed F.F.
    ;
    Alkhayyat Ahmed
    Cervical cancer is caused by the abnormal growth of female cervix cells. It is one of the most familiar factors for women's death worldwide. Therefore., early detection of cervical cancer leads to a reduced mortality rate and increased chance of being alive. The Papanicolaou is a common method for screening and identifying the cancerous cells in a woman's cervix. The resultant pap smear images may help the physician diagnose the cervix cells. The crucial part of the cell is the nucleus. Therefore., auto-detection of the nucleus is the core point in this paper. A deep learning algorithm is employed to segment the nucleus in pap smear images. Two network structures., known as ResNet18 and ResNet50., are exploited to detect the nucleus part in the cell. The results are compared with ground truth and between the two structures. Both networks., ResNet18 and ResNet50., perform almost the same., with test accuracy reaching 92%. This work distinguishes it from other work in simplicity., fast., and accuracy. Therefore., it can be recommended to be used in clinical units and rural countries which suffer from the lack of specialist physician.
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