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Vanithamani, R.
- An Improved Classification Of MR Images For Cervical Cancer Using Convolutional Neural Networks
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Authors
Affiliations
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2605-2609Abstract
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
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- Segmentation Of Carotid Artery From Intravascular Ultrasound (IVUS) Images Using Deep Learning Techniques For Plaque Identification
Abstract Views :178 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Electronics and Communication Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2638-2643Abstract
The carotid artery is the major artery that supplies blood to the brain, neck region, and face. The plaque deposition in these arteries is caused mainly due to the deposition of cholesterol, calcium, and other cellular debris carried along with the bloodstream. Hence identification of plaque is essential to avoid stroke and other diseases related to the heart. This paper proposes a deep learning-based segmentation algorithm for the identification of plaque in carotid artery using Intravascular Ultrasound (IVUS) images. To compare the performance of the proposed algorithm with the existing algorithms, evaluation metrics such as Jaccard Index (JI), Dice Similarity Coefficient (DC), and Hausdorff Distance (HD) are computed. From the results, it is observed that the proposed algorithm exhibited a high value with JI of 0.9562, DC of 0.9587, and HD of 4.8080.Keywords
Intravascular Ultrasound Image, Segmentation, Deep Learning, Jaccard Index, Hausdorff Distance, Dice CoefficientReferences
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