Mohd Aminudin Jamlos
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Mohd Aminudin Jamlos
Official Name
Mohd Aminudin , Jamlos
Alternative Name
Aminuddin Jamlos, Mohd
Jamlos, M. A.
Jamlos, Mohd Aminuddin
Jamlos, Mohd A.
Jamlos, Mohd
Main Affiliation
Scopus Author ID
36010739800
57210119953
Researcher ID
AGU-7505-2022

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  • 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
    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
    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
  • Publication
    Edge Enhancement and Detection Approach on Cervical Cytology Images
    ( 2022-09-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Mohd Wafi Nasrudin
    ;
    Mansor M.A.s.
    ;
    Alquran H.
    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. Method used in this study is the contrast enhancement technique for pre-processing and edge detection-based for segmentation of the nucleus. In this study, the average performance results of the method showed an accuracy of 96.99% in the seven-class problem using Herlev dataset. The present finding also support this study which concluded the results of accuracy achieved for the algorithm used for nucleus detection is improved by 6.15% when comparing to previous work. The accuracy value is in the lines of earlier literature that achieved accuracy of the approach used above 90% for seven class of cells. The major feature of the suggested approach is an improvement in the ability to anticipate which cells are aberrant and which are normal. Adding more classifiers could improve the suggested system even further. Therefore, a cervical cancer screening system might utilize this framework to identify women who have precancerous lesions.
      1  28