An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model
Cervical malignant growth is the fourth most typical reason for disease demise in women around the world. In developing countries, women don’t approach sufficient screening methods because of the costly procedures to undergo examination regularly, scarce awareness and lack of access to the medical c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-07-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2196114 |
| _version_ | 1797820341720973312 |
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| author | Kanimozhi T. Vijay Franklin J. |
| author_facet | Kanimozhi T. Vijay Franklin J. |
| author_sort | Kanimozhi T. |
| collection | DOAJ |
| description | Cervical malignant growth is the fourth most typical reason for disease demise in women around the world. In developing countries, women don’t approach sufficient screening methods because of the costly procedures to undergo examination regularly, scarce awareness and lack of access to the medical centre. Recently, deep learning-based radiomic methods have been introduced in differentiating vessel invasion from non-vessel invasion in Cervical Cancer (CC) by multi-parametric Magnetic Resonance Imaging (MRI). However, this model doesn’t produce sufficient results. In this work, the MRI images are initially pre-processed using bilateral filtering. After pre-processing, the image is segmented by modified U-Net model in order to identify the cancerous region. Extraction of deep semantic information from images by using residual blocks in the processes of contractions and expansions. The last layer of the contracting route uses tightly coupled convolutions in the second phase to speed up feature recycling and feature propagation. It was inferred from the observations that the proposed model was effective as a predictive tool for detecting vessel invasions in preoperative early stages of CC. Proposed model produces 94.00% detection accuracy which is better than the other existing methods. |
| first_indexed | 2024-03-13T09:36:59Z |
| format | Article |
| id | doaj.art-febd9e530b624c08876675e04e1bf4fd |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-03-13T09:36:59Z |
| publishDate | 2023-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-febd9e530b624c08876675e04e1bf4fd2023-05-25T11:41:20ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-07-0164351852810.1080/00051144.2023.2196114An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network modelKanimozhi T.0Vijay Franklin J.1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, IndiaDepartment of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, IndiaCervical malignant growth is the fourth most typical reason for disease demise in women around the world. In developing countries, women don’t approach sufficient screening methods because of the costly procedures to undergo examination regularly, scarce awareness and lack of access to the medical centre. Recently, deep learning-based radiomic methods have been introduced in differentiating vessel invasion from non-vessel invasion in Cervical Cancer (CC) by multi-parametric Magnetic Resonance Imaging (MRI). However, this model doesn’t produce sufficient results. In this work, the MRI images are initially pre-processed using bilateral filtering. After pre-processing, the image is segmented by modified U-Net model in order to identify the cancerous region. Extraction of deep semantic information from images by using residual blocks in the processes of contractions and expansions. The last layer of the contracting route uses tightly coupled convolutions in the second phase to speed up feature recycling and feature propagation. It was inferred from the observations that the proposed model was effective as a predictive tool for detecting vessel invasions in preoperative early stages of CC. Proposed model produces 94.00% detection accuracy which is better than the other existing methods.https://www.tandfonline.com/doi/10.1080/00051144.2023.2196114Cervical cancerMRI datapre-processingmodified U-net modelresidual blocks and densely connected convolutions |
| spellingShingle | Kanimozhi T. Vijay Franklin J. An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model Automatika Cervical cancer MRI data pre-processing modified U-net model residual blocks and densely connected convolutions |
| title | An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model |
| title_full | An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model |
| title_fullStr | An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model |
| title_full_unstemmed | An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model |
| title_short | An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model |
| title_sort | automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model |
| topic | Cervical cancer MRI data pre-processing modified U-net model residual blocks and densely connected convolutions |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2196114 |
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