Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

Siddhant Banyal¹, Rinky Dwivedi², Koyel Datta Gupta², Deepak Kumar Sharma^3,*, Fadi Al-Turjman⁴, Leonardo Mostarda⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1679-1696, 2021, DOI:10.32604/cmc.2021.014387

Abstract The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine… More >

Kaustubh Arun Bhavsar¹, Jimmy Singla¹, Yasser D. Al-Otaibi², Oh-Young Song^3,*, Yousaf Bin Zikria⁴, Ali Kashif Bashir⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 107-125, 2021, DOI:10.32604/cmc.2021.014604

Abstract Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry… More >

Mohamed Elhoseny¹, Mazin Abed Mohammed^2,*, Salama A. Mostafa³, Karrar Hameed Abdulkareem⁴, Mashael S. Maashi⁵, Begonya Garcia-Zapirain⁶, Ammar Awad Mutlag⁷, Marwah Suliman Maashi⁸

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 51-71, 2021, DOI:10.32604/cmc.2021.012632

Abstract Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy… More >

H. Asha Gnana Priya¹, J. Anitha¹, Daniela Elena Popescu², Anju Asokan¹, D. Jude Hemanth¹, Le Hoang Son^3,4,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2771-2786, 2021, DOI:10.32604/cmc.2021.012907

Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading… More >

Mazin Abed Mohammed¹, Karrar Hameed Abdulkareem², Begonya Garcia-Zapirain³, Salama A. Mostafa⁴, Mashael S. Maashi⁵, Alaa S. Al-Waisy¹, Mohammed Ahmed Subhi⁶, Ammar Awad Mutlag⁷, Dac-Nhuong Le^8,9,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

Phong Thanh Nguyen¹, Vy Dang Bich Huynh², Khoa Dang Vo¹, Phuong Thanh Phan¹, Eunmok Yang^3,*, Gyanendra Prasad Joshi⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2815-2830, 2021, DOI:10.32604/cmc.2021.012315

Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More >

ZICHEN LIU^1,#, SIYAO GUO^2,3,#, MENGZHI JI¹, KAILI SUN¹, ZHONGFANG LI^4,*, XIANGYU FAN^1,*

BIOCELL, Vol.44, No.4, pp. 683-694, 2020, DOI:10.32604/biocell.2020.011713

Abstract Tuberculosis (TB) remains a major cause of morbidity and mortality worldwide, particularly in developing countries. A rapid and efficient method for TB diagnosis is indispensable to check the trend of tuberculosis expansion. The emergence of drug-resistant bacteria has increased the challenge of rapid drug resistance tests. Due to its high specificity and sensitivity, bacteriophage-based diagnosis is intensively pursued. In this review, we mainly described mycobacteriophage-based diagnosis in TB detection, especially two prevalent approaches: fluorescent reporter phage and phage amplified biologically assay (PhaB). The rationale of reporter phage is that phage carrying fluorescent genes can infect host bacteria specifically. Phage amplified… More >

JIYU WANG^#, HUIPING WANG^#, YING PAN, QIANSHAN TAO, ZHIMIN ZHAI^*

BIOCELL, Vol.44, No.4, pp. 607-611, 2020, DOI:10.32604/biocell.2020.010947

Abstract Myelodysplastic syndromes (MDS) are highly heterogeneous myeloid neoplasms, and a large number of patients are difficult to diagnose and classify by blood and bone marrow examination. As a surface marker of granulocyte, studies have shown CD10 can be used to define the degree of granulocyte maturation in MDS patients. However, whether it can be used for differential diagnosis of MDS and other hematological diseases remains inconclusive. To explore the value of CD10 for differential diagnosis of MDS, 60 newly diagnosed MDS, 20 aplastic anemia (AA) patients, and 35 iron-deficient anemia (IDA) patients were selected for this study. Bone marrow (BM)… More >

Nan Geng¹, Wenxia Hu¹, Zhikun Liu², Jingwei Su², Wenyu Sun³, Shaonan Xie⁴, Cuimin Ding^1,*

Oncologie, Vol.22, No.4, pp. 245-254, 2020, DOI:10.32604/oncologie.2020.014125

Abstract Objective: This study was set out to inquire into the expression and clinical significance of lncRNA activated by transforming growth factor β (LncRNA-ATB) and in cancer tissues of patients with non-small cell lung cancer (NSCLC). Methods: LncRNA-ATB in cancer tissues and adjacent tissues of 89 NSCLC patients was detected by quantitative real-time polymerase chain reaction (qRT-PCR), and its clinical diagnostic value in NSCLC was determined by receiver operating characteristic (ROC) curves. Based on the median expression of LncRNAATB in NSCLC tissues, 89 patients were allocated into high- and low-expression groups. The 3-year survival rate was calculated using Kaplan-Meier method and… More >