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    Genetic diversity in the E6 and E7 gene of human papillomavirus type 16 among cervical cancer patients in Riau Province, Indonesia
    (2026-04-27)
    Savira, Maya
    ;
    Rachmadina
    ;
    Mahargyarani, Azza Enggar
    ;
    Admiral, Muhammad Zhafran
    ;
    S, Donel
    Cervical cancer mostly occurs due to persistent high-risk human papillomavirus (HPV), with type 16 being the most frequent. In Indonesia, cervical cancer ranks second in mortality, with a fatality rate of 57%. The E6 and E7 genes of HPV-16 play crucial roles in the virus’s oncogenic transformation, leading to cervical cancer. This study was conducted to determine the prevalence and genetic diversity of the E6 and E7 genes of HPV-16 in Riau Province, Indonesia. There were 37 HPV-positive samples analyzed using the MY09/11 primers. This was followed by the amplification of the E6 and E7 genes. Nineteen samples were detected as HPV-16, thirteen of which were coinfected with HPV-18. Seven E6 and E7 sequences were aligned compared with the reference sequence` NC_001526.4. The most common nucleotide changes in the E6 gene, 7318A>G, was detected in 30.7% of samples, leading to an amino acid change from 65N>S. Three nucleotide changes were identified in the E7 gene of sample 89: two synonymous (7831T>C, 7837T>G) and one non-synonymous (7989A>G), resulting in an amino acid change of 29N>S. The most frequent E7 nucleotide change, 7708G>A, was found in 80% of samples. Phylogenetic analysis revealed that HPV-16 isolates from Riau have close kinship with the European lineage, with 57.1% (E6) and 85.7% (E7). In conclusion, the incidence of cervical cancer in Riau Province caused by HPV-16 is 52.8% and 7318A>G and 7708G>A are the most common genetic diversity. Furthermore, the majority of HPV-16 isolates in Riau Province show close kinship with the European lineage.
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    Computer-assisted screening for cervical cancer using digital image processing of pap smear images
    (2020-03-01)
    Win, Kyi Pyar
    ;
    Kitjaidure, Yuttana
    ;
    Hamamoto, Kazuhiko
    ;
    Aung, Thet Myo
    Cervical cancer can be prevented by having regular screenings to find any precancers and treat them. The Pap test looks for any abnormal or precancerous changes in the cells on the cervix. However, the manual screening of Pap smear in the microscope is subjective with poorly reproducible criteria. Therefore, the aim of this study was to develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images. The analysis of Pap smear image is important in the cervical cancer screening system. There were four basic steps in our cervical cancer screening system. In cell segmentation, nuclei were detected using a shape-based iterative method, and the overlapping cytoplasm was separated using a marker-control watershed approach. In the features extraction step, three important features were extracted from the regions of segmented nuclei and cytoplasm. RF (random forest) algorithm was used as a feature selection method. In the classification stage, bagging ensemble classifier, which combined the results of five classifiers-LD (linear discriminant), SVM (support vector machine), KNN (k-nearest neighbor), boosted trees, and bagged trees-was applied. SIPaKMeD and Herlev datasets were used to prove the effectiveness of our proposed system. According to the experimental results, 98.27% accuracy in two-class classification and 94.09% accuracy in five-class classification was achieved using the SIPaKMeD dataset. When the results were compared with five classifiers, our proposed method was significantly better in two-class and five-class problems.
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    Cervical cancer detection and classification from pap smear images
    (2019-09-16)
    Win, Kyi Pyar
    ;
    Kitjaidure, Yuttana
    ;
    Paing, May Phyu
    ;
    Hamamoto, Kazuhiko
    In this paper, we propose a framework for detection and classification of cervical cancer from pap smear images. Early detection and accurate diagnosis of cervical cancer can reduce the death rate of cervical cancer patients. Pap smear or pap test is the most popular technique for early detection of cervical cancer. However, the manual analysis is labor intensive and time consuming process which relies on expert cytologist. Hence, it is needed to develop a computer aided diagnosis system to make the pap smear test more accurate and reliable. The objective of this paper is to present an innovative idea of applying random forest algorithm (RF) as a feature selection method using proposed bagging ensemble classifier for improving the predictive performance. The four basic steps of cervical cancer detection and classification system, image enhancement, segmentation, feature extraction and classification were used. K-means clustering combining with morphology operations obtained good segmentation for cell nuclei and cytoplasm. The most important features, shape, color and texture of nuclei and cytoplasm were applied to detect cervical cancer. To improve the accuracy of prediction results, random forest (RF) algorithm was used as a feature selection method. In classification stage, bagging ensemble classifier was applied which aggregated the results of five classifiers, linear discriminant (LD), support vector machine (SVM), weighted k-nearest neighbor (KNN), boosted trees and bagged trees. Herlev data set was used to prove the effectiveness of our proposed method. According to the experimental results, the high classification accuracy was achieved with top10 features using our proposed combined classifier. The accuracy was 97.83% in two class problem and 81.54% in seven class problem. When the results were compared with five classifiers, our proposed method was significantly better in two class and seven class problems.
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    Comparisons of pap smear classification with deep learning models
    (2019-04-01)
    Promworn, Yuttachon
    ;
    Pattanasak, Satjana
    ;
    Pintavirooj, Chuchart
    ;
    Piyawattanametha, Wibool
    We presented a comparative work of deep learning models for Pap smear classification. The benchmark parameters used to compare are accuracy, specificity, computation time, and sensitivity. Five convolution neural network models were employed to compare performance in detecting the presence of cervical precancerous or cancerous cells from a Pap smear database. The best deep learning model for multiclass classification is the densenet161 with an efficiency of 68.0% which will use to implement in our custom-made whole slide imager.
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    A comparative study of 3 deep learning models for Pap smear screening
    (2019-01-10)
    Promworn, Yuttachon
    ;
    Pintavirooj, C.
    ;
    Piyawattanametha, Wibool
    This work presents a comparative study of automated screening procedure for Pap smear with deep learning technology. Three convolution neural network models (AlexNet, densenet161 and resnet101) were employed for detecting the presence of cervical precancerous or cancerous cells from Pap smear database. The study compares accuracy, sensitivity, specificity, and computation time for each deep learning model. The best model is the densenet161 due to its high sensitivity and accuracy which are key factors in an automated Pap smear screening procedure to offer the best early detection of cervical cancer to better treatment outcomes.