Deep Learning in Analysing Paranasal Sinuses
Deep neural network-based diagnostic tools have gained state-of-the-art performance in the medical field in recent years. Diagnostic accuracy has become very critical for medical treatments. This paper proposes a simple and novel deep learning-based system for the analysis of paranasal sinuses condi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2022-06-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/31133 |
| _version_ | 1818531647021121536 |
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| author | Serkan Ozbay Orhan Tunc |
| author_facet | Serkan Ozbay Orhan Tunc |
| author_sort | Serkan Ozbay |
| collection | DOAJ |
| description | Deep neural network-based diagnostic tools have gained state-of-the-art performance in the medical field in recent years. Diagnostic accuracy has become very critical for medical treatments. This paper proposes a simple and novel deep learning-based system for the analysis of paranasal sinuses conditions. In this work, we focus on analysing the paranasal sinuses on CT images automatically, providing physicians with high-accuracy diagnosis. The proposed system enables one to reduce the number of images to be searched in a CT scan for a patient automatically, and also it provides automatic segmentation for marking and cropping the paranasal sinuses region. Thus, the proposed system significantly decreases the data required in the training phase with a gain in computational efficiency while maintaining high-accuracy performance. The proposed algorithm also makes the required segmentation automatically without manual cropping and yields outstanding performance on detecting abnormalities in the sinuses. The proposed approach has been tested on real CT images and achieved an accuracy rate of 98.52 % with a sensitivity of 100 %. |
| first_indexed | 2024-12-11T17:35:10Z |
| format | Article |
| id | doaj.art-ee3f1cb1dba44e45971eb37b3644a7b7 |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-12-11T17:35:10Z |
| publishDate | 2022-06-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-ee3f1cb1dba44e45971eb37b3644a7b72022-12-22T00:56:42ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312022-06-01283657010.5755/j02.eie.3113336387Deep Learning in Analysing Paranasal SinusesSerkan Ozbay0Orhan Tunc1Department of Electrical and Electronics Engineering, University of Gaziantep, TurkeyDepartment of Otorhinolaryngology, University of Gaziantep, TurkeyDeep neural network-based diagnostic tools have gained state-of-the-art performance in the medical field in recent years. Diagnostic accuracy has become very critical for medical treatments. This paper proposes a simple and novel deep learning-based system for the analysis of paranasal sinuses conditions. In this work, we focus on analysing the paranasal sinuses on CT images automatically, providing physicians with high-accuracy diagnosis. The proposed system enables one to reduce the number of images to be searched in a CT scan for a patient automatically, and also it provides automatic segmentation for marking and cropping the paranasal sinuses region. Thus, the proposed system significantly decreases the data required in the training phase with a gain in computational efficiency while maintaining high-accuracy performance. The proposed algorithm also makes the required segmentation automatically without manual cropping and yields outstanding performance on detecting abnormalities in the sinuses. The proposed approach has been tested on real CT images and achieved an accuracy rate of 98.52 % with a sensitivity of 100 %.https://eejournal.ktu.lt/index.php/elt/article/view/31133convolutional neural networkdeep learningmedical imagingparanasal sinus |
| spellingShingle | Serkan Ozbay Orhan Tunc Deep Learning in Analysing Paranasal Sinuses Elektronika ir Elektrotechnika convolutional neural network deep learning medical imaging paranasal sinus |
| title | Deep Learning in Analysing Paranasal Sinuses |
| title_full | Deep Learning in Analysing Paranasal Sinuses |
| title_fullStr | Deep Learning in Analysing Paranasal Sinuses |
| title_full_unstemmed | Deep Learning in Analysing Paranasal Sinuses |
| title_short | Deep Learning in Analysing Paranasal Sinuses |
| title_sort | deep learning in analysing paranasal sinuses |
| topic | convolutional neural network deep learning medical imaging paranasal sinus |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/31133 |
| work_keys_str_mv | AT serkanozbay deeplearninginanalysingparanasalsinuses AT orhantunc deeplearninginanalysingparanasalsinuses |