Advanced Medical Images Recognition and Diagnosis of Respiratory System Viruses
45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022
; : 912-917, 2022.
Artículo
en Inglés
| Scopus | ID: covidwho-1955354
ABSTRACT
Detection of respiratory viruses is a perplexing task which regularly requires saving time by taking a quick look at clinical images of patients ceaselessly. Hence, there's a need to propose and develop a model to predict the respiratory viruses (COVID-19) cases at the earliest possible to control the spread of disease. Deep learning makes it possible to find out that Covid-19 can be detected in an efficient way using its classification tools such as CNN (Convolutional Neural Network). MFCC (Mel Frequency Cepstral Coefficients) is a very common and efficient technique for signal processing. In this research, a MFCC - CNN learning model to hasten the prediction process is proposed that assist the medical professionals. MFCC is used for extracting the image's features concerning existence of COVID-19 or not. Classification is performed by using convolutional neural network. This makes the time-consuming process easier and faster with more accurate results for radiologists and this reduces the spread of virus and save lives. Experimental results shows that using CT image converted to Mel-frequency cepstral coefficient spectrogram images as input to a CNN can achieve a high accuracy results;with classification of validation data scoring an accuracy of 99.08% correct classification of COVID and NON COVID labeled images. Hence, it can be used practically for detection of COVID-19 from CT images. The work here provides a proof of concept that high accuracy can be achieved with a moderate dataset, which can have a significant impact in this area. © 2022 Croatian Society MIPRO.
CNN; Computed tomography; COVID-19; feature extraction; image classification; Keywords - Biomedical imaging; MFCC; Computer aided diagnosis; Computerized tomography; Convolution; Convolutional neural networks; Deep learning; Disease control; Medical imaging; Microelectronics; Viruses; Biomedical imaging; Convolutional neural network; CT Image; Features extraction; High-accuracy; Images classification; Keyword - biomedical imaging; Mel frequency cepstral co-efficient; Mel-frequency cepstral coefficients
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Idioma:
Inglés
Revista:
45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022
Año:
2022
Tipo del documento:
Artículo
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