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
    A narrative review: Classification of pap smear cell image for cervical cancer diagnosis
    ( 2020-01-01)
    Wan Azani Wan Mustafa
    ;
    Halim A.
    ;
    Ab Rahman K.S.
    Cervical cancer develops as cells transformation in the cervix of a female that connects the uterus to the vagina. This cancer may impact the columnal epithelial cells of the cervix and therefore can be expanded to the lymphatic and circulatory system (metastasize), sometimes the kidneys, liver, prostate, vagina, and rectum. Many of the cervical cancer patients survived by taking early prevention by undergoing a Pap Smear Test. However, the result of the test usually takes a few weeks which is extremely time-consuming especially at the government hospital. The purpose of this research was to study the detection and classification method of the Pap Smear image to resolve the time-consuming issues and support better system performance to prevent low precision result of the Human Papilloma Virus (HPV) stages. A few studies were considered which features the cell image databases to classify cervical cancer according to its type. Besides, the classification system and the performance of the preceding papers that had been considered include a few features found in the cell images. Those features were the size of the cells, the shape of the cells, the colour, Region of Interest (ROI) and overlapped cell nuclei. The other existing design methods being considered were the Deep Convolutional Neural Network (CNN) and the Artificial Neural Network (ANN). These findings technique showed the highest percentage of the system accuracy, precision, and specificity that might be excellent for further analysis. The research limitation was the method of how the numerous image databases needed to be processed and classified one at a time. None of these articles stated whether they had found the way to compute more images at once. The aim of the study was to review the previous paper in order to define the feature datasets that needed to be considered. The features were important in designing a new classification method and increasing the performance of the systems. The features included the nucleus shape, diameter and surface areas, colour and luminosity of the cell datasets, the region of the nucleus, design and image resolution. In this paper, an extensive analysis was studied for cervical cancer classification techniques. As expected from the outcome, the study of the feature database, the classification method and the system performance were reviewed deeper for further assessments.
  • Publication
    Cervical Cancer Classification Using Image Processing Approach: A Review
    ( 2020-09-21)
    Zhe Wei L.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Hamzari Sahabudin M.
    At present, Cervical cancer is the second most common cancer among women around the world. This cancer develops in the cervix; which is the entrance to the uterus. Most of the time, hospital doctors are facing difficulties in identifying cancer cells because the nucleus is sometimes rather difficult to see with the naked eye. Normal cells nuclei are smaller than abnormal cells nuclei. Abnormal nuclei are larger, which sometimes cannot be precisely identified by classifying stages of cervical cancer with the naked eye. This is because each doctor has a different perspective to monitor the classification of cancer stages by observing the nucleus without precisely reducing the size of the classification accuracy. Lately, many researchers have proposed methods for detection and classification of Pap smear images to diagnose cervical cancer. This approach can improve detection and classification accuracy, resulting in better results with accurate data balance and samples. Some patients are found to be in Stage 2 but after retesting they are actually in Stage 4, where the chances of recovery are very low. This is because doctors cannot find the right balance data and unable to take samples properly. This article discusses a comprehensive review of cervical recognition based on segment core and classification.