Covid-19 ultrasound image classification using svm based on kernels deduced from convolutional neural network
| dc.contributor.author | Al-jumaili, Saif | |
| dc.contributor.author | Duru, Adil Deniz | |
| dc.date.accessioned | 2022-02-08T08:38:40Z | |
| dc.date.available | 2022-02-08T08:38:40Z | |
| dc.date.issued | 2021 | en_US |
| dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | Abstract— Millions of people are infected daily with Coronavirus to this day, which increases deaths daily, that has made the virus an epidemic. Based on the current crisis, the availability of tool kits for test plays a significant role in fighting against Covid-19. According to less of availability tools and time consume by using traditional medical tools kit, that provide motivation for researchers to use the advantages of artificial intelligence (AI) techniques. Due to the ability of integrated with medical imaging, AI is very useful for precise diagnosis and classification for different types of diseases. However, in this study, we introduce an idea that combines a set of pre-trained deep learning convolutional neural network models with a supervised machine learning classifier, Supporting Vector Machines (SVM). The dataset used in this study was Lung ultrasound (LUS). To extract features from images, we utilized four types of CNN models namely (Resnet18, Resnet50, GoogleNet, and NASNet-Mobile). Depending on the experimental outcomes, our proposed method show outperform compared to the other latest papers published. Our results achieved based on the four types of evaluation metrics which are Accuracy, Precision, Recall, and F1-Score, where all evaluations achieved exceeded of 99%. | en_US |
| dc.identifier.citation | Al-Jumaili, S., Duru, A. D., Uçan, O. N. (2021). Covid-19 ultrasound image classification using SVM based on kernels deduced from convolutional neural network. In 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (429-433). IEEE. | en_US |
| dc.identifier.scopus | 2-s2.0-85123310569 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/2252 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Uçan, Osman Nuri | |
| dc.language.iso | en | |
| dc.relation.isversionof | 10.1109/ISMSIT52890.2021.9604551 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Ulusal - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Convolutional Neural Network | en_US |
| dc.subject | Feature Extraction | en_US |
| dc.subject | SVM | en_US |
| dc.subject | Classification | en_US |
| dc.title | Covid-19 ultrasound image classification using svm based on kernels deduced from convolutional neural network | |
| dc.type | Conference Object |
Dosyalar
Lisans paketi
1 - 1 / 1
[ X ]
- İsim:
- license.txt
- Boyut:
- 1.44 KB
- Biçim:
- Item-specific license agreed upon to submission
- Açıklama:
