Yang Zhang¹, Liru Qiu², Yongkai Zhu¹, Long Wen^1,*, Xiaoping Luo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 873-894, 2022, DOI:10.32604/cmes.2022.022322

Abstract Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4… More >

Hussah Nasser AlEisa¹, El-Sayed M. El-kenawy^2,3, Amel Ali Alhussan^1,*, Mohamed Saber⁴, Abdelaziz A. Abdelhamid^5,6, Doaa Sami Khafaga¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447

Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then… More >

Md. Saiful Islam, Shuvo Jyoti Das, Md. Riajul Alam Khan, Sifat Momen^*, Nabeel Mohammed

Computer Systems Science and Engineering, Vol.44, No.1, pp. 519-534, 2023, DOI:10.32604/csse.2023.025282

Abstract COVID-19 has created a panic all around the globe. It is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), originated from Wuhan in December 2019 and spread quickly all over the world. The healthcare sector of the world is facing great challenges tackling COVID cases. One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases. In this article, we propose a deep Convolutional Neural Network (CNN) based approach to detect COVID+ (i.e., patients with COVID-19), pneumonia and normal cases, from the chest X-ray images. COVID-19 detection… More >

M. M. Lotfy¹, Hazem M. El-Bakry², M. M. Elgayar³, Shaker El-Sappagh^4,5, G. Abdallah M. I¹, A. A. Soliman¹, Kyung Sup Kwak^6,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1141-1158, 2022, DOI:10.32604/cmc.2022.024193

Abstract Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people, and its high number of deaths also by 7%. For that purpose, a proposed model of several stages was developed. The first stage is optimizing the images using dynamic adaptive histogram equalization, performing a semantic segmentation using DeepLabv3Plus, then augmenting the data by flipping it horizontally, rotating it, then flipping it vertically. The second stage builds a custom convolutional neural network model using several pre-trained ImageNet. Finally, the model compares the pre-trained data to the new output, while repeatedly… More >

Yurananatul Mangalmurti, Naruemon Wattanapongsakorn^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 733-752, 2022, DOI:10.32604/iasc.2022.025073

Abstract This paper presents effective techniques for automatic detection/classification of COVID-19 and other lung diseases using machine learning, including deep learning with convolutional neural networks (CNN) and classical machine learning techniques. We had access to a large number of chest X-ray images to use as input data. The data contains various categories including COVID-19, Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition, chest X-ray images with many findings (abnormalities and diseases) from the National Institutes of Health (NIH) was also considered. Our deep learning approach used a CNN architecture with VGG16 and VGG19 models which were pre-trained with ImageNet. We… More >

Cemile Bagkur^1,*, Emrah Guler^2,3, Bilgen Kaymakamzade^4,5, Evren Hincal^4,5, Kaya Suer⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 111-132, 2022, DOI:10.32604/cmes.2022.016957

Abstract While antibiotic resistance is becoming increasingly serious today, there is almost no doubt that more challenging times await us in the future. Resistant microorganisms have increased in the past decades leading to limited treatment options, along with higher morbidity and mortality. Klebsiella pneumoniae is one of the significant microorganisms causing major public health problems by acquiring resistance to antibiotics and acting as an opportunistic pathogen of healthcare-associated infections. The production of extended spectrum beta-lactamases (ESBL) is one of the resistance mechanisms of K. pneumoniae against antibiotics. In this study, the future clinical situation of ESBL-producing K. pneumoniae was investigated in… More >

Kamaledin Abodayeh¹, Ali Raza^2,3,*, Muhammad Rafiq⁴, Muhammad Shoaib Arif⁵, Muhammad Naveed⁵, Zunir Zeb³, Syed Zaheer Abbas³, Kiran Shahzadi³, Sana Sarwar³, Qasim Naveed³, Badar Ul Zaman³, Muhammad Mohsin⁶

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6073-6088, 2022, DOI:10.32604/cmc.2022.020732

Abstract Pneumonia is a highly transmissible disease in children. According to the World Health Organization (WHO), the most affected regions include south Asia and sub-Saharan Africa. Worldwide, 15% of pediatric deaths can be attributed to pneumonia. Computing techniques have a significant role in science, engineering, and many other fields. In this study, we focused on the efficiency of numerical techniques via computer programs. We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques. We discuss two types of analysis: dynamical and numerical. The dynamical analysis included positivity, boundedness, local stability, reproduction number, and equilibria of the model.… More >

YANYAN LI¹, SHAHANAVAJ KHAN^2,3,4, ANIS AHMAD CHAUDHARY⁵, HASSAN AHMED RUDAYNI⁵, ABDUL MALIK², ASHWAG SHAMI⁶

BIOCELL, Vol.46, No.1, pp. 87-95, 2022, DOI:10.32604/biocell.2022.016509

Abstract Available reports have confirmed a link between bacterial infection and the progression of different types of cancers, including colon, lungs, and prostate cancer. Here we report the Chlamydia pneumonia proteins targeting in endoplasmic reticulum (ER) using in-silico approaches and their possible role in lung cancer etiology. We predicted 48 proteins that target human ER, which may be associated with protein folding and protein-protein interactions during infection. The results showed C. pneumoniae proteins targeting human ER and their implications in lung cancer growth. These targeted proteins may be involved in competitive interactions between host and bacterial proteins, which may change the… More >

Saiful Izzuan Hussain^*, Nadiah Ruza

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1143-1156, 2022, DOI:10.32604/iasc.2022.020508

Abstract Coronavirus (COVID-19) is a pandemic disease classified by the World Health Organization. This virus triggers several coughing problems (e.g., flu) that include symptoms of fever, cough, and pneumonia, in extreme cases. The human sputum or blood samples are used to detect this virus, and the result is normally available within a few hours or at most days. In this research, we suggest the implementation of automated deep learning without require handcrafted expertise of data scientist. The model developed aims to give radiologists a second-opinion interpretation and to minimize clinicians’ workload substantially and help them diagnose correctly. We employed automated deep… More >

Sami Gazzah^1,*, Rida Bayi², Soulaimane Kaloun², Omar Bencharef²

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 677-692, 2022, DOI:10.32604/iasc.2022.019360

Abstract A new virus called SARS-CoV-2 appeared at the end of space 2019 in Wuhan, China. This virus immediately spread throughout the world due to its highly contagious nature. Moreover, SARS-CoV-2 has changed the way of our life and has caused a huge economic and public health disaster. Therefore, it is urgent to identify positive cases as soon as possible and treat them as isolated. Automatic detection of viruses using computer vision and machine learning will be a valuable contribution to detecting and limiting the spread of this epidemic. The delay introduction of X-ray technology as diagnostic tool limited our ability… More >