ICTACT Journal on Image and Video Processing

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An Improved Classification Of MR Images For Cervical Cancer Using Convolutional Neural Networks


Affiliations
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India
     

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Cervical cancer is the biggest cause of death in the field of women gynaecology. Patient treatment outcomes are influenced by the stage and nodal status of their cancers as well as their tumour size and histological classes. In this paper, we develop a classification model using a state-of-art heuristic mechanism that enables the use of deep learning algorithm to classify the MRI image from the input cervical images. The classification is conducted with highly dense network that helps to reduce the errors during the testing process. The simulation is conducted in matlab to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher grade of classification accuracy than the other existing methods.

Keywords

Classification, MR Image, Cervical Cancer, CNN
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  • A. Ghoneim, G. Muhammad and H.S. Hossain, “Cervical Cancer Classification using Convolutional Neural Networks and Extreme Learning Machines”, Future Generation Computer Systems, Vol. 102, pp. 643-649, 2020.

  • S. Karthick, P.A. Rajakumari and R.A. Raja, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.

  • N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.

  • A. Khadidos, A.O. Khadidos, S. Kannan and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-20, 2020.

  • X. Tan, K. Li, J. Zhang and W. Wang, “Automatic Model for Cervical Cancer Screening based on Convolutional Neural Network: A Retrospective, Multicohort, Multicenter Study”, Cancer Cell International, Vol. 21, No. 1, pp. 1-10, 2021.

  • V. Maheshwari, M.R. Mahmood and S. Sravanthi, “Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-13, 2021.

  • S.B. Sangeetha, R. Sabitha and B. Dhiyanesh, “Resource Management Framework using Deep Neural Networks in Multi-Cloud Environment”, Proceedings of International Conference on Operationalizing Multi-Cloud Environments, pp. 89-104, 2021.

  • N. Bnouni, H.B. Amor and I. Rekik, “Boosting CNN Learning by Ensemble Image Preprocessing Methods for Cervical Cancer Segmentation”, Proceedings of 18th International Multi-Conference on Systems, Signals and Devices, pp. 264-269, 2021.

  • T. Haryanto, I.S. Sitanggang, M.A. Agmalaro and R. Rulaningtyas, “The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification”, Proceedings of International Conference on Computer Engineering, Network, and Intelligent Multimedia, pp. 34-38, 2020.

  • Y. Xiang, W. Sun, C. Pan and M. Yan, “A Novel Automation-Assisted Cervical Cancer Reading Method based on Convolutional Neural Network”, Biocybernetics and Biomedical Engineering, Vol. 40, No. 2, pp. 611-623, 2020.

  • H. Akbar, N. Anwar, S. Rohajawati and A. Yulfitri, “Optimizing AlexNet using Swarm Intelligence for Cervical Cancer Classification”, Proceedings of International Symposium on Electronics and Smart Devices, pp. 1-6, 2021.

  • K. Deepa, “A Journal on Cervical Cancer Prediction using Artificial Neural Networks”, Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 2, pp. 1085-1091, 2021.

  • L. Cao, J. Yang and Z. Rong, “A Novel Attention-Guided Convolutional Network for the Detection of Abnormal Cervical Cells in Cervical Cancer Screening”, Medical Image Analysis, Vol. 73, pp. 102197-102210, 2021.

  • S. Murugan, C. Venkatesan, M.G. Sumithra and S. Manoharan, “DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images”, IEEE Access, Vol. 9, pp. 90319-90329, 2021.

  • N. Dong, L. Zhao and C.H. Wu, “Inception V3 based Cervical Cell Classification Combined with Artificially Extracted Features”, Applied Soft Computing, Vol. 93, pp. 106311-106319, 2020.

  • G. Liang, H. Hong and W. Zheng, “Combining Convolutional Neural Network with Recursive Neural Network for Blood Cell Image Classification”, IEEE Access, Vol. 6, pp. 36188-36197, 2018.

  • B. Wang, Y. Zhang, C. Wu and F. Wang, “Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm”, Contrast Media and Molecular Imaging, Vol. 23, pp. 1-16, 2021.

  • C. Zhang, C.W. Jia, H.R. Ge, “Quantitative Detection of Cervical Cancer based on Time Series Information from Smear Images”, Applied Soft Computing, Vol. 112, pp. 107791-107798, 2021.


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  • An Improved Classification Of MR Images For Cervical Cancer Using Convolutional Neural Networks

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Authors

S. Gowri
Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India
Judith Justin
Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India
R. Vanithamani
Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India

Abstract


Cervical cancer is the biggest cause of death in the field of women gynaecology. Patient treatment outcomes are influenced by the stage and nodal status of their cancers as well as their tumour size and histological classes. In this paper, we develop a classification model using a state-of-art heuristic mechanism that enables the use of deep learning algorithm to classify the MRI image from the input cervical images. The classification is conducted with highly dense network that helps to reduce the errors during the testing process. The simulation is conducted in matlab to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher grade of classification accuracy than the other existing methods.

Keywords


Classification, MR Image, Cervical Cancer, CNN

References