Walid El-Shafai¹, Abeer D. Algarni^2,*, Ghada M. El Banby³, Fathi E. Abd El-Samie^1,2, Naglaa F. Soliman^2,4

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1561-1575, 2022, DOI:10.32604/iasc.2022.020386

Abstract Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of… More >

Junding Sun^1,3,#, Xiang Li^1,#, Chaosheng Tang^1,*, Shixin Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 729-753, 2021, DOI:10.32604/cmes.2021.016416

Abstract Purpose: As to January 11, 2021, coronavirus disease (COVID-19) has caused more than 2 million deaths worldwide. Mainly diagnostic methods of COVID-19 are: (i) nucleic acid testing. This method requires high requirements on the sample testing environment. When collecting samples, staff are in a susceptible environment, which increases the risk of infection. (ii) chest computed tomography. The cost of it is high and some radiation in the scan process. (iii) chest X-ray images. It has the advantages of fast imaging, higher spatial recognition than chest computed tomography. Therefore, our team chose the chest X-ray images as the experimental dataset in… More >

Ahmed Reda^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809

Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal… More >

Shubham Mahajan^1,*, Akshay Raina², Mohamed Abouhawwash^3,4, Xiao-Zhi Gao⁵, Amit Kant Pandit¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1541-1556, 2022, DOI:10.32604/cmc.2022.019496

Abstract Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination… More >

Walid El-Shafai¹, Anas M. Ali^1,2, El-Sayed M. El-Rabaie¹, Naglaa F. Soliman^3,*, Abeer D. Algarni³, Fathi E. Abd El-Samie^1,3

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1141-1157, 2022, DOI:10.32604/cmc.2022.018547

Abstract In developing countries, medical diagnosis is expensive and time consuming. Hence, automatic diagnosis can be a good cheap alternative. This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks (CNNs). These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists. The deep CNNs allow direct learning from the medical images. However, the accessibility of classified data is still the largest challenge, particularly in the field of medical imaging. Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs… More >

Anas Basalamah¹, Shadikur Rahman^2,*

Computer Systems Science and Engineering, Vol.40, No.1, pp. 375-388, 2022, DOI:10.32604/csse.2022.016949

Abstract This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19 (Negative or Positive).… More >

Saddam Bekhet^1,*, Monagi H. Alkinani², Reinel Tabares-Soto³, M. Hassaballah⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2475-2491, 2021, DOI:10.32604/cmc.2021.018514

Abstract The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial intelligence (AI) techniques have been developed… More >

Sajib Sarker, Ling Tan^*, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh

Journal on Internet of Things, Vol.3, No.2, pp. 39-51, 2021, DOI:10.32604/jiot.2021.014877

Abstract The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained architectures such as VGG16, VGG19,… More >

Munif Alotaibi^1,*, Bandar Alotaibi²

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 885-898, 2021, DOI:10.32604/iasc.2021.018350

Abstract The novel coronavirus 2019 (COVID-19) is one of a large family of viruses that cause illness, the symptoms of which range from a common cold, fever, coughing, and shortness of breath to more severe symptoms. The virus rapidly and easily spreads from infected people to others through close contact in the absence of protection. Early detection of COVID-19 assists governmental authorities and healthcare specialists in reducing the chain of transmission and flattening the curve of the pandemic. The widespread form of the COVID-19 diagnostic test lacks a high true positive rate and a low false negative rate and needs a… More >

Yousef Sanajalwe^1,2,*, Mohammed Anbar¹, Salam Al-E’mari¹

Computer Systems Science and Engineering, Vol.39, No.1, pp. 15-35, 2021, DOI:10.32604/csse.2021.017191

Abstract The novel coronavirus disease 2019 (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people. The first step to mitigate and control its spread is to identify and isolate the infected people. But, because of the lack of reverse transcription polymerase chain reaction (RT-CPR) tests, it is important to discover suspected COVID-19 cases as early as possible, such as by scan analysis and chest X-ray by radiologists. However, chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case. In this paper, an automated novel detection… More >