Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach
Novel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of t...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1008652 |
| _version_ | 1797996702968315904 |
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| author | Piyush Kumar Pareek Prasath Alais Surendhar S Ram Prasad Govindaraj Ramkumar Ekta Dixit R. Subbiah Saleh H. Salmen Hesham S. Almoallim S. S. Priya S. Arockia Jayadhas |
| author_facet | Piyush Kumar Pareek Prasath Alais Surendhar S Ram Prasad Govindaraj Ramkumar Ekta Dixit R. Subbiah Saleh H. Salmen Hesham S. Almoallim S. S. Priya S. Arockia Jayadhas |
| author_sort | Piyush Kumar Pareek |
| collection | DOAJ |
| description | Novel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of the research is to increase the effectiveness of cervical cancer patients' detection by using deep learning-based radiomics assessment of magnetic resonance imaging (MRI). From March 2016 to November 2019, 125 patients with early-stage cervical cancer provided 980 dynamic X1 contrast-enhanced (X1DCE) and 850 X2 weighted imaging (X2WI) MRI images for training and testing. A convolutional neural network model was used to estimate cervical cancer state based on the specified characteristics. The X1DCE exhibited high discriminative outcomes than X2WI MRI in terms of prediction ability, as calculated by the confusion matrix assessment and receiver operating characteristic (ROC) curve approach. The mean maximum region under the curve of 0.95 was found using an attentive ensemble learning method that included both MRI sequencing (Sensitivity = 0.94, Specificity = 0.94, and accuracy = 0.96). Whenever compared with conventional radiomic approaches, the results show that a variety of radiomics based on deep learning might be created to help radiologists anticipate vascular invasion in patients with cervical cancer before surgery. Based on radiomics technique, it has proven to be an effective tool for estimating cervical cancer in its early stages. It would help people choose the best therapy method for them and make medical judgments. |
| first_indexed | 2024-04-11T10:21:35Z |
| format | Article |
| id | doaj.art-85dc679018244e6f8d235f05eb0c02a2 |
| institution | Directory Open Access Journal |
| issn | 1687-8442 |
| language | English |
| last_indexed | 2024-04-11T10:21:35Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj.art-85dc679018244e6f8d235f05eb0c02a22022-12-22T04:29:44ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/1008652Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning ApproachPiyush Kumar Pareek0Prasath Alais Surendhar S1Ram Prasad2Govindaraj Ramkumar3Ekta Dixit4R. Subbiah5Saleh H. Salmen6Hesham S. Almoallim7S. S. Priya8S. Arockia Jayadhas9Department of Computer Science and Engineering and Head of IPR CellDepartment of Biomedical EngineeringDepartment of BotanyDepartment of Electronics and Communication EngineeringDepartment of Computer ScienceDepartment of Mechatronics EngineeringDepartment of Botany and MicrobiologyDepartment of Oral and Maxillofacial SurgeryDepartment of Microbiology - ImmunologyDepartment of EECENovel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of the research is to increase the effectiveness of cervical cancer patients' detection by using deep learning-based radiomics assessment of magnetic resonance imaging (MRI). From March 2016 to November 2019, 125 patients with early-stage cervical cancer provided 980 dynamic X1 contrast-enhanced (X1DCE) and 850 X2 weighted imaging (X2WI) MRI images for training and testing. A convolutional neural network model was used to estimate cervical cancer state based on the specified characteristics. The X1DCE exhibited high discriminative outcomes than X2WI MRI in terms of prediction ability, as calculated by the confusion matrix assessment and receiver operating characteristic (ROC) curve approach. The mean maximum region under the curve of 0.95 was found using an attentive ensemble learning method that included both MRI sequencing (Sensitivity = 0.94, Specificity = 0.94, and accuracy = 0.96). Whenever compared with conventional radiomic approaches, the results show that a variety of radiomics based on deep learning might be created to help radiologists anticipate vascular invasion in patients with cervical cancer before surgery. Based on radiomics technique, it has proven to be an effective tool for estimating cervical cancer in its early stages. It would help people choose the best therapy method for them and make medical judgments.http://dx.doi.org/10.1155/2022/1008652 |
| spellingShingle | Piyush Kumar Pareek Prasath Alais Surendhar S Ram Prasad Govindaraj Ramkumar Ekta Dixit R. Subbiah Saleh H. Salmen Hesham S. Almoallim S. S. Priya S. Arockia Jayadhas Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach Advances in Materials Science and Engineering |
| title | Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach |
| title_full | Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach |
| title_fullStr | Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach |
| title_full_unstemmed | Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach |
| title_short | Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach |
| title_sort | predicting the spread of vessels in initial stage cervical cancer through radiomics strategy based on deep learning approach |
| url | http://dx.doi.org/10.1155/2022/1008652 |
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