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 >

Mudiyala Aparna, Battula Srinivasa Rao^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796

Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into… More >

Muhammad Javaid Iqbal¹, Muhammad Waseem Iqbal², Muhammad Anwar^3,*, Muhammad Murad Khan⁴, Abd Jabar Nazimi⁵, Mohammad Nazir Ahmad⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5267-5281, 2023, DOI:10.32604/cmc.2023.033024

Abstract The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred… More >

Zeynep Ekicioglu Kuzeci^1,*, Surkay D. Akbarov^2,3

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4337-4362, 2023, DOI:10.32604/cmc.2023.033446

Abstract The present work investigates the mechanically forced vibration of the hydro-elasto-piezoelectric system consisting of a two-layer plate “elastic+PZT”, a compressible viscous fluid, and a rigid wall. It is assumed that the PZT (piezoelectric) layer of the plate is in contact with the fluid and time-harmonic linear forces act on the free surface of the elastic-metallic layer. This study is valuable because it considers for the first time the mechanical vibration of the metal+piezoelectric bilayer plate in contact with a fluid. It is also the first time that the influence of the volumetric concentration of the constituents on the vibration of… More >

Abdullah A. Asiri¹, Bilal Khan², Fazal Muhammad^3,*, Shams ur Rahman⁴, Hassan A. Alshamrani¹, Khalaf A. Alshamrani¹, Muhammad Irfan⁵, Fawaz F. Alqhtani¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 299-312, 2023, DOI:10.32604/iasc.2023.032426

Abstract In the medical profession, recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality. The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans. Automatic (computerized) illness detection in medical imaging has found you the emergent region in several medical diagnostic applications. Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio. The brain tumor is one of the most common causes of death. Researchers have already proposed various models for the classification… More >

Abdullah A. Asiri¹, Ahmad Shaf^2,*, Tariq Ali², Muhammad Aamir², Ali Usman², Muhammad Irfan³, Hassan A. Alshamrani¹, Khlood M. Mehdar⁴, Osama M. Alshehri⁵, Samar M. Alqhtani⁶

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 127-143, 2023, DOI:10.32604/iasc.2023.032391

Abstract The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique in which two neural networks… More >

Rehana Ghulam¹, Sammar Fatima¹, Tariq Ali¹, Nazir Ahmad Zafar¹, Abdullah A. Asiri², Hassan A. Alshamrani^2,*, Samar M. Alqhtani³, Khlood M. Mehdar⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1333-1349, 2023, DOI:10.32604/cmc.2023.031695

Abstract Human brain consists of millions of cells to control the overall structure of the human body. When these cells start behaving abnormally, then brain tumors occurred. Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts. To handle this issue, various deep learning techniques for brain tumor detection and segmentation techniques have been developed, which worked on different datasets to obtain fruitful results, but the problem still exists for the initial stage of detection of brain tumors to save human lives. For this purpose, we proposed a novel U-Net-based Convolutional… More >

Shaopeng Li, Peng Wang, Jun Qiu, Yiju Xie, Dawei Yin, Kexue Deng^*

Oncologie, Vol.24, No.3, pp. 527-538, 2022, DOI:10.32604/oncologie.2022.022815

Abstract Background: To compare the differential diagnostic value of iterative decomposition of water and fat with the echo asymmetrical and least-squares estimation quantitation sequence (IDEAL-IQ) with that of intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) in differentiating between alpha fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH). Materials and Methods: A total of 28 AFP-negative HCC cases and 15 FNH cases were scanned using the IDEAL-IQ and IVIM-DWI magnetic resonance imaging (MRI) protocols. Two radiologists independently assessed the fat fraction (FF) and the iron level surrogate (R2*) derived from the IDEAL-IQ images and the apparent diffusion coefficient (ADC), pure… More >

Benjamin Zielonka^1,2,*, David M. Harrild^1,2, Sunil J. Ghelani^1,2, Eleni G. Elia^1,2, Christopher W. Baird^3,4, Andrew J. Powell^1,2, Rahul H. Rathod^1,2

Congenital Heart Disease, Vol.17, No.5, pp. 591-603, 2022, DOI:10.32604/chd.2022.021509

Abstract Background: The right ventricle to pulmonary artery conduit (RVPAC) may impair right ventricular (RV) function in patients with functional single right ventricles. Modification of the RVPAC using a ring-reinforced end with dunked insertion into the RV through a limited ventriculotomy may reduce the impact on RV function. We compared RV segmental strain between patients with a traditional RVPAC and ring-reinforced RVPAC using feature tracking cardiovascular magnetic resonance (CMR) imaging. Methods: Patients with CMR examinations after Stage I operation with RVPAC between 2000 and 2018 were reviewed. Ventricular mass, volumes, late gadolinium enhancement (LGE), and peak radial and circumferential strain of… More >

R. Radha^1,*, R. Gopalakrishnan²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3113-3127, 2023, DOI:10.32604/iasc.2023.030667

Abstract In the field of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity. Locating the defective cells precisely during the diagnosis phase helps to fight the greatest exterminator of mankind. Early detection of these defective cells requires an accurate computer-aided diagnostic system (CAD) that supports early treatment and promotes survival rates of patients. An earlier version of CAD systems relies greatly on the expertise of radiologist and it consumed more time to identify the defective region. The manuscript takes the… More >