Classification of Positive COVID-19 CT Scans using Deep Learning
نویسندگان
چکیده
In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity classifiers an accurate diagnosis. response to coronavirus 2019 (COVID-19) pandemic, new testing procedures, treatments, and vaccines being developed rapidly. One potential diagnostic tool is reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically time-consuming process, was less sensitive COVID-19 recognition in disease’s early stages. Here we introduce optimized deep learning (DL) scheme distinguish COVID-19-infected patients from normal according computed tomography (CT) scans. proposed method, contrast enhancement used improve quality original images. A pretrained DenseNet-201 DL model then trained using transfer learning. Two fully connected layers average pool feature extraction. The extracted Firefly algorithm select most optimal features. Fusing selected important improving accuracy approach; however, it directly affects computational cost technique. parallel high index technique fuse two vectors; outcome passed on extreme machine final classification. Experiments were conducted collected database 70:30 training: Testing ratio. Our results indicated classification 94.76% approach. comparison outcomes several other models demonstrated effectiveness our method classifying based CT
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ژورنال
عنوان ژورنال: Computers, materials & continua
سال: 2021
ISSN: ['1546-2218', '1546-2226']
DOI: https://doi.org/10.32604/cmc.2021.013191