Wan Azani Wan Mustafa
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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
    Cervical Cancer Detection Techniques: A Chronological Review
    ( 2023-05-01)
    Wan Azani Wan Mustafa
    ;
    Ismail S.
    ;
    Mokhtar F.S.
    ;
    Alquran H.
    ;
    Al-Issa Y.
    Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.
  • 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
    Pap Smear Image Analysis Based on Nucleus Segmentation and Deep Learning – A Recent Review
    ( 2023-02-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Ismail S.
    ;
    Alquran H.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    Cervical cancer refers to a dangerous and common illness that impacts women worldwide. Moreover, this cancer affects over 300,000 people each year, with one woman diagnosed every minute. It affects over 0.5 million women annually, leading to over 0.3 million deaths. Recently, considerable literature has grown around developing technologies to detect cervical cancer cells in women. Previously, a cervical cancer diagnosis was made manually, which may result in a false positive or negative. Automated detection of cervical cancer and analysis method of the Papanicolaou (Pap) smear images are still debated among researchers. Thus, this paper reviewed several studies related to the detection method of Pap smear images focusing on Nuclei Segmentation and Deep Learning (DL) from the publication year of 2020, 2021, and 2022. Training, validation, and testing stages have all been the subject of study. However, there are still inadequacies in the current methodologies that have caused limitations to the proposed approaches by researchers. This study may inspire other researchers to view the proposed methods' potential and provide a decent foundation for developing and implementing new solutions.
  • Publication
    Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach
    ( 2022-01-01)
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Qasmieh I.A.
    ;
    Yacob Y.M.
    ;
    Alsalatie M.
    ;
    Al-Issa Y.
    ;
    Alqudah A.M.
    Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The success of this proposed system in distinguishing between the levels of normal cases with 100% accuracy and 100% sensitivity. On top of that, it can distinguish between normal and abnormal cases with an accuracy of 100%. The high level of abnormality is then studied and classified with a high accuracy. On the other hand, the low level of abnormality is studied separately and classified into two classes, mild and moderate dysplasia, with ∼ 92% accuracy. The proposed system is a built-in cascading manner with five models of polynomial (SVM) classifier. The overall accuracy in training for all cases is 100%, while the overall test for all seven classes is around 92% in the test phase and overall accuracy reaches 97.3%. The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer, which may lead to an increase in the survival rate in women.
  • Publication
    Contrast enhancement on pap smear cell images: A comparison
    ( 2023-06-12)
    Hameed M.S.S.
    ;
    Wan Azani Wan Mustafa
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    Cervical cancer is a common disease that can be carried by women. It is the 3rd leading cause of female cancer in Malaysia and the 4th most common type of cancer for women globally. Referring to the HPV Information Centre in 2018, it is estimated that 1682 women are diagnosed and 944 die from having cervical cells. In 2018, 569, 847 of the 18,078,957 cases were categorised as cervical cancer, which is 3.2%. With the advancement in science and technology, cervical cancer can be detected at an early stage by conducting a Pap smear test. This test will filter abnormal cervical cells and detect precancerous changes in cervical cells based on the colour and shape properties of their nuclei and cytoplasm. The problem is that performing the procedure manually can be time-consuming and cause inconsistencies and errors even further because the cervical cell itself does not show an obvious difference in texture and colour from normal cells. This paper will focus on colour contrast enhancement of the cervical cell using a few methods such as Contrast Stretching (CS), CLAHE, Histogram Equalization (HE), Image Adjustment, and Multi Scale Retinex (MSR). From this research, the cervical cell colour contrast can be enhanced to a better level and the detection of cervical cells can happen faster and more accurately. Hence, the errors in detecting can be reduced again so they can be treated soon.
  • Publication
    Pap Smear Images Classification Using Machine Learning: A Literature Matrix
    ( 2022-12-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Hanafi H.F.
    ;
    Ismail S.
    ;
    Rahman K.S.A.
    Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.
      19  1
  • Publication
    Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
    ( 2022-11-01)
    Alsalatie M.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Yasmin Mohd Yacob
    ;
    Alayed A.A.
    The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors’ increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.
      19  1
  • Publication
    A Recent Systematic Review of Cervical Cancer Diagnosis: Detection and Classification
    ( 2022-09-01)
    Wan Azani Wan Mustafa
    ;
    Alias N.A.
    ;
    Mohd Aminudin Jamlos
    ;
    Ismail S.
    ;
    Alquran H.
    Women around the world are frequently diagnosed with cervical cancer. In the beginning, there are no symptoms for the fourth most common cause of fatality in women. Cells of cervical cancer develop gradually at the cervix. Several studies have mentioned that early detection of cervical tumor is very important for the cancer to be properly treated and to make sure the cancer can be successfully treated while minimizing deaths due to cervical cancer. The diagnosis of such cancer before it spread fast is currently a pressing issue for healthcare professionals. The systematic analysis has many benefits above conventional literature reviews. These evaluations can be improved by having a more defined review procedure, a more important topic of study, and fundamental priorities that can control research bias. This also provides a comprehensive understanding of the physical characteristics of the healthy and unhealthy cervix and aids in early treatment planning by giving detailed information about one another. Utilizing image segmentation, a number of techniques are employed to find malignancy. The dataset contains four distinct pathological pictures, including normal, malignancy, and high-grade squamous intraepithelial lesions (HSIL). While pap tests are the most popular way to diagnose cervical cancer, their accuracy depends a lot on how well cytotechnicians can use brightfield microscopy to spot abnormal cells on smears.
      1  17
  • Publication
    A review of detection and classification cervical cell images
    ( 2023-06-12)
    Nahrawi N.
    ;
    Wan Azani Wan Mustafa
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Mohd Aminudin Jamlos
    ;
    Ismail S.
    ;
    Alquran H.
    ;
    Alqudah A.M.
    Cervical cancer is a very prevalent disease among women all over the world. Cervical cancer can form in the cervix cells found in the lower uterus. Women all over the world are at death risk as a result of this type of cancer. Cervical cancer has seven stages: normal intermediate, normal superficial, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. Doctors in hospitals find it difficult to recognise cancer cells as it is challenging to view a nucleus through the naked eye. A normal cell's nucleus is smaller than an abnormal cell's nucleus. It is possible to calculate the size of the abnormal nucleus with the naked eye in order to assess the stages of cervical cancer. A tool for identifying and quantifying Pap smear cell images to detect cervical cancer has recently been suggested by several researchers. This method has the potential to increase detection and classification precision, resulting in improved results with balanced data and samples. A comprehensive study of nucleus detection cervical cancer classification techniques was conducted in this paper. As a result of the findings, the function database, detection and classification process, and device performance were all investigated for further evaluation.
      16  4
  • 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