Ayyaz Hussain¹, Mohammed Alawairdhi², Fayez Alazemi³, Sajid Ali Khan⁴, Muhammad Ramzan^2,*

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 857-871, 2020, DOI:10.32604/iasc.2020.010120

Abstract The Computer Aided Diagnosis (CAD) systems are gaining more recognition and being used as an aid by clinicians for detection and interpretation of diseases every passing day due to their increasing accuracy and reliability. The lung(s) nodule detection is a very crucial and difficult step for CAD systems. In this paper, a hybrid approach for the lung nodule detection using a deformable model and distance transform has been proposed. The proposed method has the ability to detect all major kinds of nodules such as the juxta-plueral, isolated, and the juxta-vescular, along with the non-solid nodules automatically and intelligently. Results show… More >

G. Arun Sampaul Thomas¹, Y. Harold Robinson², E. Golden Julie³, Vimal Shanmuganathan⁴, Seungmin Rho⁵, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1613-1629, 2021, DOI:10.32604/cmc.2020.013443

Abstract Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain and it cannot be detected with a naked eye. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses. The CNN model is trained by different images of eyes that have retinopathy and those which do… More >

K. Shankar^1,*, Eswaran Perumal¹, Mohamed Elhoseny², Phong Thanh Nguyen³

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1665-1680, 2021, DOI:10.32604/cmc.2020.013251

Abstract Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The… More >

Gopi Krishna Durbhaka¹, Barani Selvaraj¹, Mamta Mittal², Tanzila Saba^3,*, Amjad Rehman³, Lalit Mohan Goyal⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131

Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

Fadhila Mansour^1,2,*, Abdelhak Lakehal^2,3, Lahcène Nezzal^2,3

Oncologie, Vol.22, No.3, pp. 117-128, 2020, DOI:10.32604/oncologie.2020.014979

Abstract The objective of this study was to quantify the delays in access to diagnosis and treatment of women with breast cancer who are managed at the anticancer center of Batna, Algeria. This was a descriptive retrospective study conducted during the period August 2015 to February 2016. In order to trace the history of the journey from the first signs of cancer to the first treatments, a questionnaire was filled in from the medical files and completed by an interview with the patients. A total of 267 patients were included in the study. The median time to management was 4.5 months.… More >

Sotiria C. Apostolopoulou^1,*, Stella Brili², Eftihia Sbarouni³, Dimitris Tousoulis², Konstantinos Toutouzas²

Congenital Heart Disease, Vol.15, No.5, pp. 369-375, 2020, DOI:10.32604/CHD.2020.012092

Abstract Objective: Acquired coronary artery disease, initially thought to rarely affect survivors of congenital heart disease, is increasingly recognized in this population, as these patients grow in age and numbers in the recent era. This study reports our experience with coronary artery disease in adults with congenital heart disease and discusses treatment issues and the existing literature. Methods: Retrospective review of all charts of adults with congenital heart disease and acquired coronary artery disease was performed. Patients’ clinical characteristics, diagnosis, risk factors, noninvasive and invasive imaging and management data were recorded. Results: Coronary artery disease was diagnosed at 35–70 of age… More >

Gustaf Tanghöj^1,*, Anna Lindam², Petru Liuba^3,4, Gunnar Sjöberg⁵, Estelle Naumburg¹

Congenital Heart Disease, Vol.15, No.5, pp. 287-299, 2020, DOI:10.32604/CHD.2020.011977

Abstract Objective: Secundum atrial septal defect (ASD II) is a common congenital heart defect, and interatrial communications among preterm children is even more common. The objective of this study was to calculate the incidence of ASD II in children, with assessment to gestational age at birth. Further, to assess maternal, prenatal and postnatal risk factors associated with ASD II among children of different gestational age at birth. Design: This national registry based retrospective incidence study was supplemented with a national case-control study, using the Swedish Register of Congenial Heart Disease, Swedish Medical Birth Register and Statistics Sweden. All children, 0–18 years… More >

Fei Yang^1,2,#, Xiaoqing Zhang^1,*,#, Yong Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 365-382, 2020, DOI:10.32604/cmes.2020.010798

Abstract Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms (ECG) signals. Over the past years, deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems (CADs), but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods. To tackle this problem, this work proposes a convolutional neural network (CNN) model named PDNet to recognize different types of heart arrhythmias efficiently. In the PDNet, a convolutional block named PDblock is devised, which is comprised of a pointwise convolutional layer… More >

Tahir Abbas Khan¹, Sagheer Abbas¹, Allah Ditta², Muhammad Adnan Khan^{3, *}, Hani Alquhayz⁴, Areej Fatima³, Muhammad Farhan Khan⁵

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2591-2605, 2020, DOI:10.32604/cmc.2020.011892

Abstract The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables… More >

Dan Zhang¹, Shengyong Fu^2,*, Jie Xu³, Xia Sun¹

Oncologie, Vol.22, No.2, pp. 95-105, 2020, DOI:10.32604/oncologie.2020.012439

Abstract This study aimed to explore the expression and clinical significance of LINC01197 in the serum of patients with pancreatic cancer (PC). Methods: A total of 50 PC patients (patient group) treated in our hospital from March 2012 to April 2014 were collected, and another 50 healthy people (normal group) were collected for physical examination. The expression of LINC01197 in the serum of the two groups was detected by qRT-PCR method, and the expression of CA-199 in serum was detected by Roche automatic biochemistry. The expression and diagnostic values of CA-199 and LINC01197 in PC were analyzed, and the relationship between… More >