Muhammad Naeem Akram¹, Muhammad Usman Yaseen¹, Muhammad Waqar¹, Muhammad Imran^1,*, Aftab Hussain²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855

Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >

Liwei Deng¹, Yuanzhi Zhang¹, Xin Yang^2,*, Sijuan Huang², Jing Wang^3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642

Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm combines a meta-learning approach with… More >

Quan Yan¹, Yunfan Ye¹, Jing Xia¹, Zhiping Cai^1,*, Zhilin Wang², Qiang Ni³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2545-2558, 2023, DOI:10.32604/iasc.2023.029857

Abstract Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction speed. In particular, conventional reconstruction… More >

Azhar Tursynova¹, Batyrkhan Omarov^1,2, Natalya Tukenova^3,*, Indira Salgozha⁴, Onergul Khaaval³, Rinat Ramazanov⁵, Bagdat Ospanov⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1431-1446, 2023, DOI:10.32604/cmc.2023.034400

Abstract In the field of stroke imaging, deep learning (DL) has enormous untapped potential. When clinically significant symptoms of a cerebral stroke are detected, it is crucial to make an urgent diagnosis using available imaging techniques such as computed tomography (CT) scans. The purpose of this work is to classify brain CT images as normal, surviving ischemia or cerebral hemorrhage based on the convolutional neural network (CNN) model. In this study, we propose a computer-aided diagnostic system (CAD) for categorizing cerebral strokes using computed tomography images. Horizontal flip data magnification techniques were used to obtain more accurate categorization. Image Data Generator… More >

Maryam Wardah¹, Muhammad Mateen^1,*, Tauqeer Safdar Malik², Mohammad Eid Alzahrani³, Adil Fahad³, Abdulmohsen Almalawi⁴, Rizwan Ali Naqvi⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2283-2299, 2023, DOI:10.32604/cmc.2023.030706

Abstract Brain hemorrhage is a serious and life-threatening condition. It can cause permanent and lifelong disability even when it is not fatal. The word hemorrhage denotes leakage of blood within the brain and this leakage of blood from capillaries causes stroke and adequate supply of oxygen to the brain is hindered. Modern imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI) are employed to get an idea regarding the extent of the damage. An early diagnosis and treatment can save lives and limit the adverse effects of a brain hemorrhage. In this case, a deep neural network (DNN)… More >

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

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 to its simplicity of usage,… 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

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 malformations were compared.… 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

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