RESUMEN
Respiratory infections are a confusing and time-consuming task that caused recently a pandemic that affected the whole world. One of the pandemics was COVID-19 that has exposed the vulnerability of medical services across the world, particularly in underdeveloped nations. There comes a strong demand for developing new computer-assisted diagnosis tools to present cost-effective and rapid screening in locations wherein enormous traditional testing is impossible. Medical imaging becomes critical for diagnosing disease, X-rays and computed tomography (CT) scan are employed in the deep network which will be helpful in diagnosing diseases. This paper proposes a scanning model based on using a Mel Frequency Cepstral Coefficients (MFCC) features extracted from a respiratory virus CT-Scan image and then filtered by applying Gabor filter (GF). The filtered image is passed to Convolutional Neural Network (CNN) for classifying the image for the presence of a respiratory virus such as Covid, Viral Pneumonia or being a healthy normal image. The proposed system achieved a validation accuracy of 100% with an overall accuracy of 99.44%. © 2022 IEEE.
RESUMEN
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.