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In Biomedical signal processing and control

Limitations of available literature : Nowadays, coronavirus disease 2019 (COVID-19) is the world-wide pandemic due to its mutation over time. Several works done for covid-19 detection using different techniques however, the use of small datasets and the lack of validation tests still limit their works. Also, they depend only on the increasing the accuracy and the precision of the model without giving attention to their complexity which is one of the main conditions in the healthcare application. Moreover, the majority of healthcare applications with cloud computing use centralization transmission process of various and vast volumes of information what make the privacy and security of personal patient's data easy for hacking. Furthermore, the traditional architecture of the cloud showed many weaknesses such as the latency and the low persistent performance.

Method proposed by the author with technical information : In our system, we used Discrete Wavelet transform (DWT) and Principal Component Analysis (PCA) and different energy tracking methods such as Teager Kaiser Energy Operator (TKEO), Shannon Wavelet Entropy Energy (SWEE), Log Energy Entropy (LEE) for preprocessing the dataset. For the first step, DWT used to decompose the image into coefficients where each coefficient is vector of features. Then, we apply PCA for reduction the dimension by choosing the most essential features in features map. Moreover, we used TKEO, SHEE, LEE to track the energy in the features in order to select the best and the most optimal features to reduce the complexity of the model. Also, we used CNN model that contains convolution and pooling layers due to its efficacity in image processing. Furthermore, we depend on deep neurons using small kernel windows which provide better features learning and minimize the model's complexity.The used DWT-PCA technique with TKEO filtering technique showed great results in terms of noise measure where the Peak Signal-to-Noise Ratio (PSNR) was 3.14 dB and the Signal-to-Noise Ratio (SNR) of original and preprocessed image was 1.48, 1.47 respectively which guaranteed the performance of the filtering techniques.The experimental results of the CNN model ensure the high performance of the proposed system in classifying the covid-19, pneumonia and normal cases with 97% of accuracy, 100% of precession, 97% of recall, 99% of F1-score, and 98% of AUC.

Advantages and application of proposed method : The use of DWT-PCA and TKEO optimize the selection of the optimal features and reduce the complexity of the model.The proposed system achieves good results in identifying covid-19, pneumonia and normal cases.The implementation of fog computing as an intermediate layer to solve the latency problem and computational cost which improve the Quality of Service (QoS) of the cloud.Fog computing ensure the privacy and security of the patients' data.With further refinement and validation, the IFC-Covid system will be real-time and effective application for covid-19 detection, which is user friendly and costless.

Mohamed Akram Khelili, Sihem Slatnia, Okba Kazar, Harous Saad


Cloud computing, Covid-19, Deep learning, Fog computing, Internet of Medical Things (IoMT), Quality of Service (QoS)