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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  • 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