DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES

Hanife Göker

doi:10.18038/estubtda.1384489

TR EN

DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES

Abstract

Cervical cancer is a common and serious cancer affecting more than half a million women worldwide. For cervical cancer disease management, prognosis prediction, or optimizing medical intervention, early detection of the disease is critical. It is one of the types of cancer that can be successfully treated, as long as it is diagnosed early and managed effectively. In this study, an image processing-based solution was proposed for the diagnosis of cervical cancer from uterine cervix images using transfer learning architectures to reduce the workload and assist the experts. The proposed transfer learning model was tested using a publicly available dataset, which includes 917 uterine cervix images. Uterine cervix images were enhanced and brightness level using the histogram equalization method and denoised using the Gaussian filter. Then, the performances of AlexNet, DenseNet201, MobilenetV2, Resnet50, Xception, and VGG19 transfer learning architectures were compared. The transfer learning model performance was evaluated using the 10-fold cross-validation method. VGG19 transfer learning algorithm had the highest performance. VGG19 transfer learning algorithm achieved 98.26% accuracy, 0.9671 f1-measure, 0.9896 specificity, 0.9631 sensitivity, 0.9711 precision, 0.9552 Matthews correlation coefficient (MCC), and 0.955 kappa statistic. The combination of histogram equalization, Gaussian filter, and the VGG19 transfer learning approach can be used for accurate and efficient detection of cervical cancer from uterine cervix images. In this study, more accuracy was achieved compared to the known related studies in the literature.

Keywords

Image processing, Transfer learning, Deep learning, Cervix cancer, Gaussian filter

DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES

Abstract

Cervical cancer is a common and serious cancer affecting more than half a million women worldwide. For cervical cancer disease management, prognosis prediction, or optimizing medical intervention, early detection of the disease is critical. It is one of the types of cancer that can be successfully treated, as long as it is diagnosed early and managed effectively. In this study, an image processing-based solution was proposed for the diagnosis of cervical cancer from uterine cervix images using transfer learning architectures to reduce the workload and assist the experts. The proposed transfer learning model was tested using a publicly available dataset, which includes 917 uterine cervix images. Uterine cervix images were enhanced and brightness level using the histogram equalization method and denoised using the Gaussian filter. Then, the performances of AlexNet, DenseNet201, MobilenetV2, Resnet50, Xception, and VGG19 transfer learning architectures were compared. The transfer learning model performance was evaluated using the 10-fold cross-validation method. VGG19 transfer learning algorithm had the highest performance. VGG19 transfer learning algorithm achieved 98.26% accuracy, 0.9671 f1-measure, 0.9896 specificity, 0.9631 sensitivity, 0.9711 precision, 0.9552 Matthews correlation coefficient (MCC), and 0.955 kappa statistic. The combination of histogram equalization, Gaussian filter, and the VGG19 transfer learning approach can be used for accurate and efficient detection of cervical cancer from uterine cervix images. In this study, more accuracy was achieved compared to the known related studies in the literature.

Keywords

Image processing, Transfer learning, Deep learning, Cervix cancer, Gaussian filter

References

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Details

Primary Language

English

Subjects

Information Systems (Other)

Journal Section

Research Article

Authors

Hanife Göker ^*
0000-0003-0396-7885
Türkiye

Publication Date

June 28, 2024

Submission Date

November 1, 2023

Acceptance Date

March 1, 2024

Published in Issue

Year 2024 Volume: 25 Number: 2

DOI

https://doi.org/10.18038/estubtda.1384489

IZ

https://izlik.org/JA95CB79SG

APA

Göker, H. (2024). DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 25(2), 222-239. https://doi.org/10.18038/estubtda.1384489

AMA

1.Göker H. DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES. Estuscience - Se. 2024;25(2):222-239. doi:10.18038/estubtda.1384489

Chicago

Göker, Hanife. 2024. “DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25 (2): 222-39. https://doi.org/10.18038/estubtda.1384489.

EndNote

Göker H (June 1, 2024) DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25 2 222–239.

IEEE

[1]H. Göker, “DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES”, Estuscience - Se, vol. 25, no. 2, pp. 222–239, June 2024, doi: 10.18038/estubtda.1384489.

ISNAD

Göker, Hanife. “DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25/2 (June 1, 2024): 222-239. https://doi.org/10.18038/estubtda.1384489.

JAMA

1.Göker H. DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES. Estuscience - Se. 2024;25:222–239.

MLA

Göker, Hanife. “DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 25, no. 2, June 2024, pp. 222-39, doi:10.18038/estubtda.1384489.

Vancouver

1.Hanife Göker. DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES. Estuscience - Se. 2024 Jun. 1;25(2):222-39. doi:10.18038/estubtda.1384489

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