Yudong Zhang¹, Muhammad Attique Khan², Ziquan Zhu¹, Shuihua Wang^1,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172 - 20 January 2023

Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due… More >

Wenlei Qian^1,#, Xinzhu Zhou^2,#, Ke Shi¹, Li Jiang¹, Xi Liu³, Liting Shen¹, Zhigang Yang^1,*

Congenital Heart Disease, Vol.18, No.1, pp. 113-125, 2023, DOI:10.32604/chd.2023.023542 - 09 January 2023

Abstract Background: Pulmonary atresia (PA) is a group of heterogeneous complex congenital heart disease. Only one study modality might not get a correct diagnosis. This study aims to investigate the diagnostic power of dual-source computed tomography (DSCT) for all intracardiac and extracardiac deformities in patients with PA compared with transthoracic echocardiography (TTE). Materials and Methods: This retrospective study enrolled 79 patients and divided them into three groups according to their main diagnosis. All associated malformations and clinical information, including treatments, were recorded and compared among the three groups. The diagnostic power of DSCT and TTE on all associated… More > Graphic Abstract

Yanwen Lu^1,#, Wenliang Ma^1,#, Xiang Dong^1,#, Mackenzie Brown², Tong Lu^3,*, Weidong Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 347-362, 2023, DOI:10.32604/cmes.2023.024909 - 05 January 2023

Abstract This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework More >

Iftikhar Naseer^1,*, Tehreem Masood¹, Sheeraz Akram¹, Arfan Jaffar¹, Muhammad Rashid², Muhammad Amjad Iqbal³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2039-2054, 2023, DOI:10.32604/cmc.2023.032927 - 22 September 2022

Abstract Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic… More >

Alberto Cipriani^1,#, Pietro Bernardo Dall’Aglio^1,#, Laura Mazzotta¹, Domenico Sirico², George Sarris³, Mark Hazekamp⁴, Thierry Carrel⁵, Alessandro Frigiola⁶, Vladimir Sojak⁴, Mauro Lo Rito⁶, Jurgen Horer⁷, Regine Roussin⁷, Julie Cleuziou⁸, Bart Meyns⁹, Jose Fragata¹⁰, Helena Telles¹⁰, Anastasios C. Polimenakos¹¹, Katrien Francois¹², Altin Veshti¹³, Jukka Salminen¹⁴, Alvaro Gonzalez Rocafort¹⁵, Matej Nosal¹⁶, Eleftherios Protopapas³, Roberto Tumbarello¹⁷, Patrizio Sarto¹⁸, Cinzia Pegoraro¹⁸, Raffaella Motta¹⁹, Giovanni Di Salvo², Domenico Corrado¹, Vladimiro L. Vida¹, Massimo A. Padalino^1,2,*

Congenital Heart Disease, Vol.17, No.4, pp. 375-385, 2022, DOI:10.32604/chd.2022.019385 - 04 July 2022

Abstract Background: Anomalous aortic origin of a coronary artery (AAOCA) is a congenital heart disease with a 0.3%−0.5% prevalence. Diagnosis is challenging due to nonspecific clinical presentation. Risk stratification and treatment are currently based on expert consensus and single-center case series. Methods: Demographical and clinical data of AAOCA patients from 17 tertiary-care centers were analyzed. Diagnostic imaging studies (Bidimensional echocardiography, coronary computed tomography angiography [CCTA] were collected. Clinical correlations with anomalous coronary course and origin were evaluated. Results: Data from 239 patients (42% males, mean age 15 y) affected by AAOCA were collected; 154 had AAOCA involving the… More > Graphic Abstract

Tong Pang^#, Li Jiang^#, Yi Zhang, Mengxi Yang, Jin Wang, Yuan Li^*, Zhigang Yang^*

Congenital Heart Disease, Vol.17, No.4, pp. 479-489, 2022, DOI:10.32604/chd.2022.020401 - 04 July 2022

Abstract Background: To evaluate the qualitative and quantitative differences between intracardiac and extracardiac vascular malformations in patients with a single atrium (SA), single ventricle (SV) and single atrium-single ventricle (SA-SV) using dual-source CT (DSCT), and to compare the diagnostic performances of DSCT and transthoracic echocardiography (TTE). Methods: This retrospective study included 24 SA, 75 SV and 24 SA-SV patients who underwent both DSCT and TTE before surgery. The diagnostic values of DSCT and TTE for intracardiac and extracardiac malformations were compared according to the surgical results. The diameters of the major artery and vein were measured and calculated… More >

Joddat Fatima^1,*, Amina Jameel², Muhammad Usman Akram³, Adeel Muzaffar Syed¹, Malaika Mushtaq³

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1489-1504, 2022, DOI:10.32604/iasc.2022.025935 - 25 May 2022

Abstract The long twisted fragile tube, termed as spinal cord, can be named as the second vital organ of Central Nervous System (CNS), after brain. In human anatomy, all crucial life activities are controlled by CNS. The spinal cord does not only control the flow of information from the brain to rest of the body, but also takes charge of our reflexes control and the mobility of body. It keeps the body upright and acts as the main support for the flesh and bones. Spine deformity can occur by birth, due to aging, injury or spine… More >

S. Priyadarsini^1,*, Carlos Andrés Tavera Romero², Abolfazl Mehbodniya³, P. Vidya Sagar⁴, Sudhakar Sengan⁵

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1057-1068, 2022, DOI:10.32604/csse.2022.024788 - 09 May 2022

Abstract In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the… More >

Nabila Mansouri^1,2,*, Khalid Sultan³, Aakash Ahmad⁴, Ibrahim Alseadoon⁴, Adal Alkhalil⁴

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1247-1264, 2022, DOI:10.32604/iasc.2022.025046 - 03 May 2022

Abstract The outbreak of novel Coronavirus COVID-19, an infectious disease caused by the SARS-CoV-2 virus, has caused an unprecedented medical, economic, and social emergency that requires data-driven intelligence and decision support systems to counter the subsequent pandemic. Data-driven models and intelligent systems can assist medical researchers and practitioners to identify symptoms of COVID-19 infection. Several solutions based on medical image processing have been proposed for this purpose. However, the most shortcoming of hand craft image processing systems is the lower provided performances. Hence, for the first time, the proposed solution uses a deep learning model that… More >

S. Jeevitha^1,*, K. Valarmathi²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1123-1139, 2022, DOI:10.32604/iasc.2022.024746 - 03 May 2022

Abstract Pulmonary Embolism is a blood clot in the lung which restricts the blood flow and reduces blood oxygen level resulting in mortality if it is untreated. Further, pulmonary embolism is evidenced prominently in the segmental and sub-segmental regions of the computed tomography angiography images in COVID-19 patients. Pulmonary embolism detection from these images is a significant research problem in the challenging COVID-19 pandemic in the venture of early disease detection, treatment, and prognosis. Inspired by several investigations based on deep learning in this context, a two-stage framework has been proposed for pulmonary embolism detection which… More >