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Search Results (102)
  • Open Access

    ARTICLE

    Novel Framework of Segmentation 3D MRI of Brain Tumors

    Ibrahim Mahmoud El-Henawy1, Mostafa Elbaz2, Zainab H. Ali3,*, Noha Sakr4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3489-3502, 2023, DOI:10.32604/cmc.2023.033356

    Abstract Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image.… More >

  • Open Access

    ARTICLE

    Pixel’s Quantum Image Enhancement Using Quantum Calculus

    Husam Yahya1, Dumitru Baleanu2,3,4, Rabha W. Ibrahim5,*, Nadia M.G. Al-Saidi6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2531-2539, 2023, DOI:10.32604/cmc.2023.033282

    Abstract The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research… More >

  • Open Access

    ARTICLE

    Fusion Strategy for Improving Medical Image Segmentation

    Fahad Alraddady1, E. A. Zanaty2, Aida H. Abu bakr3, Walaa M. Abd-Elhafiez4,5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3627-3646, 2023, DOI:10.32604/cmc.2023.027606

    Abstract In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of More >

  • Open Access

    ARTICLE

    An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

    Jun Liu1, Geng Yuan2, Changdi Yang2, Houbing Song3, Liang Luo4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195

    Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Models for Magnetic Resonance Imaging (MRI)-Based Brain Tumor Classification

    Abdullah A. Asiri1, Bilal Khan2, Fazal Muhammad3,*, 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… More >

  • Open Access

    ARTICLE

    Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

    Abdullah A. Asiri1, Ahmad Shaf2,*, 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… More >

  • Open Access

    ARTICLE

    A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

    Zahid Ullah1,*, Mona Jamjoom2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1457-1473, 2023, DOI:10.32604/cmc.2023.032752

    Abstract Alzheimer’s disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD’s progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD.… More >

  • Open Access

    ARTICLE

    Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

    Farah Mohammad*, Saad Al-Ahmadi

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552

    Abstract

    There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based

    More >

  • Open Access

    ARTICLE

    Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

    Zakarya Farea Shaaf1,*, Muhammad Mahadi Abdul Jamil1, Radzi Ambar1, Ahmed Abdu Alattab2,3, Anwar Ali Yahya3,4, Yousef Asiri4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1819-1835, 2023, DOI:10.32604/cmc.2023.031900

    Abstract The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN)… More >

  • Open Access

    ARTICLE

    Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

    Tianyuan Jia1, Xiangyong Chen1,2,*, Xiurong Yao1,*, Feng Zhao1, Jianlong Qiu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 221-239, 2023, DOI:10.32604/cmes.2022.020780

    Abstract Fixed-time synchronization (FTS) of delayed memristor-based neural networks (MNNs) with discontinuous activations is studied in this paper. Both continuous and discontinuous activations are considered for MNNs. And the mixed delays which are closer to reality are taken into the system. Besides, two kinds of control schemes are proposed, including feedback and adaptive control strategies. Based on some lemmas, mathematical inequalities and the designed controllers, a few synchronization criteria are acquired. Moreover, the upper bound of settling time (ST) which is independent of the initial values is given. Finally, the feasibility of our theory is attested More >

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