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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Cervical Cancer Classification Using Image Processing Approach: A Review

2020-09-21 , Zhe Wei Low , 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.

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Knowledge of Human Papillomavirus (HPV) and cervical cancer among Malaysia residents: a review

2020 , Nadzirah Bt Nahrawi , Wan Azani Wan Mustafa , Siti Nurul Aqmariah Mohd Kanafiah

Cervical cancer is ranks as the third leading cause of female cancer deaths among women in Malaysia. Most of the cervical cases are caused by Human Papillomavirus (HPV) infection. To prevent HPV infection, Malaysia Government had implement Human Papillomavirus (HPV) vaccination program to all secondary school girls from 13 years old and above. The focus in this paper was to review the article based on the knowledge on HPV and cervical cancer among Malaysia resident before and after the implementation of HPV vaccine program. The knowledge about HPV, HPV vaccine, and cervical cancer after the implementation of national HPV vaccination program is better compare to before the program to be implemented. However, the knowledge is still poor among the respondents although there is an improvement after the program been implemented. The respondent gives a positive attitude towards HPV vaccination and cervical cancer screening. The main barrier of vaccination and Pap smear test are side effects, risk, cost, and effectiveness. In conclusion, knowledge about HPV and cervical cancer is really important among women. Education programs to the public are needed to enhance knowledge and to control the illness.

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Stages Classification on Cervical Cell Images: A Comparative Study

2023 , Wan Azani Wan Mustafa , Mohamad Irfan Noor , Alquran Hiam , Miharaini Md Ghani , Hafizul Fahri Hanafi , Noor Hidayah Che Lah , Mundher Adnan M. , Hameed Abdul Hussein Abbas

The cancer of the cervix is called cervical cancer. An element of a woman's womb is the cervix. Among other diseases that affect women, it came in at number four on the list. According to the World Health Organization's cancer report, there are currently roughly 10 million new cases of cancer recorded year, and by 2020, that number will have doubled to 20 million. With the right screening and awareness campaign, this number can be cut in half. A quarter of cancers are said to be brought on by infections, including hepatitis B, which is connected to liver cancer, and the human papillomavirus, which is connected to cervix cancer. Deep learning techniques have been successfully applied to a wide range of image classification tasks, and have the potential to be highly effective for cervical cell image classification as well. In this project, we propose to use a deep learning-based approach to classify cervical cell images into different categories, such as normal cells, abnormal cells, or cancerous cells. To achieve this goal, we will first pre-process the images to prepare them for analysis, and then extract relevant features. These features will be used to train a deep learning model, which will be fine-tuned and optimized for the specific task of cervical cell classification. In this project, transfer learning method will be by using pre-trained classifier such as ResNet-50, GoogLeNet and EfficientNet-b0. We will evaluate the performance of the model using metrics such as accuracy and compare our results to those obtained using traditional machine learning approaches. From this project, the highest accuracy achieved are 51.49%. The goal to develop a pre-trained classifier transfer learning can be used to accurately and reliably classify cervical cell images in a clinical setting are achieved.

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