COVID-19 image classification using deep features and fractional-order marine predators algorithm; Scientific Reports; Vol. 10, iss. 1
| Parent link: | Scientific Reports Vol. 10, iss. 1.— 2020.— [15364, 10 p.] |
|---|---|
| Autor corporatiu: | |
| Altres autors: | , , , , , |
| Sumari: | Title screen Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images. |
| Idioma: | anglès |
| Publicat: |
2020
|
| Matèries: | |
| Accés en línia: | https://doi.org/10.1038/s41598-020-71294-2 |
| Format: | Electrònic Capítol de llibre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668323 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 668323 | ||
| 005 | 20260807071632.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\39548 | ||
| 035 | |a RU\TPU\network\39380 | ||
| 090 | |a 668323 | ||
| 100 | |a 20221004d2020 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a COVID-19 image classification using deep features and fractional-order marine predators algorithm |f A. T. Sahlol, D. Yousri, A. A. Ewees [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 59 tit.] | ||
| 330 | |a Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images. | ||
| 461 | |t Scientific Reports | ||
| 463 | |t Vol. 10, iss. 1 |v [15364, 10 p.] |d 2020 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a computational models | |
| 610 | 1 | |a image processing | |
| 610 | 1 | |a machine learning | |
| 610 | 1 | |a вычислительные модели | |
| 610 | 1 | |a обработка изображений | |
| 610 | 1 | |a машинное обучение | |
| 701 | 1 | |a Sahlol |b A. T. |g Ahmed | |
| 701 | 1 | |a Yousri |b D. |g Dalia | |
| 701 | 1 | |a Ewees |b A. A. |g Ahmed | |
| 701 | 1 | |a Al-qaness |b M. A. A. |g Mohammed | |
| 701 | 1 | |a Damasevicius |b R. |g Robertas | |
| 701 | 1 | |a Mokhamed Elsaed (Mohamed Abd Elaziz) |b A. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1987- |g Akhmed Mokhamed |3 (RuTPU)RU\TPU\pers\46943 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |c 2017- |x TPU |7 ca |8 rus |9 28330 |
| 801 | 0 | |a RU |b 63413507 |c 20221004 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1038/s41598-020-71294-2 | |
| 942 | |c CF | ||