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

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 is proposed for the classification… 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

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 nodule detection method in CT… 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

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 right coronary artery… 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

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 based on DSCT… 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

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 surgery. In this research article,… 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

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 Fully Automatic Segmentation (FAS) technique… 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

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 is applied to Computed Tomography… More >

S. Jeevitha^1,*, K. Valarmathi²

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

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 is realized as a segmentation… More >

S. Priyadarsini^1,*, Carlos Andrés Tavera Romero², M. Mrunalini³, Ganga Rama Koteswara Rao⁴, Sudhakar Sengan⁵

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1517-1530, 2022, DOI:10.32604/iasc.2022.024786

Abstract A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region… More >

Xiaorui Zhang^1,2,3,*, Jie Zhou², Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589

Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification… More >