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65 articles for “MRI imaging”
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Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Alzheimer Detection Using MRI Imaging Modality
Abstract: Alzheimer’s disease (AD) is a neurological disease that affects memory and livelihood of the people that are diagnosed with it. Many different imaging modalities have been used to help diagnose the disease. Each of these modalities offers something different towards the detection and possible treatments for AD. In this project, we developed a new approach based on mathematical and image processing techniques for better classification of AD. We proposed to …
Published in Current Trends in Signal Processing · Vol. 6, Issue 1, 2016 · pp. 11–17 Read article
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Development of Intensity-based Segmentation Technique for Meningioma Tumor Detection in MRI Images
Abstract: There are many types of brain tumors. Some brain tumors detection system using segmentation and classification of MRI images. Brain tumors can have any shape or cut. This encourages us to use high-capacity deep neural networks. Segmentation task and 8000 images for classification task of our neural network and found the best architecture to use. convolutional neural network. In recent years, the three most common forms of brain tumours—glioma, meningioma, …
Published in Research and Reviews : Journal of Computational Biology · Vol. 11, Issue 03, 2022 · pp. 50–54 Read article
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Detection of Brain Tumors from MRI Images Based On Development of Thinking Computer Systems Techniques
Abstract: Brain tumors are one of the common diseases of the nervous system and have great harm to human health, and even lead to death. The detection, segmentation, and extraction of contaminated tumour regions from Magnetic Resonance Imaging (MRI) pictures are major problems; yet, a repetitive and time-consuming task performed by radiologists or clinical experts relies on their experience. The many anatomical structures of the human organ can be imagined using …
Published in Current Trends in Signal Processing Read article
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Detection of Brain Tumors from MRI Images based On Development of Thinking Computer systems Techniques
Abstract: Brain tumors are one of the common diseases of the nervous system and have great harm to human health, and even lead to death. The detection, segmentation, and extraction of contaminated tumour regions from Magnetic Resonance Imaging (MRI) pictures are major problems; yet, a repetitive and time-consuming task performed by radiologists or clinical experts relies on their experience. The many anatomical structures of the human organ can be imagined using …
Published in Current Trends in Signal Processing · Vol. 11, Issue 3, 2021 · pp. 28–34 Read article
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Application of Compressive Sensing for Sampling and Reconstruction of MRI Images
Abstract: In recent years, a new theory of compressive sensing has evolved which asserts that super resolved signals and images can be recovered with far fewer samples than that demanded by the Nyquist sampling theorem. It is required that the signal being sensed has a low information-rate meaning that it is sparse in original or some transform domain. Former approaches capture the complete signal and process it to extract the information. …
Published in Current Trends in Signal Processing · Vol. 6, Issue 2, 2016 · pp. 42–48 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 1–9 Read article
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Brain Tumor Detection by Aggregating Deep Learning and GAN Models for Faster MRI image Synthesis
Abstract: Brain tumors comprise a global health challenge that, in order to be treated and organized, need early and accurate diagnosis. Usually conducted through medical imaging, brain tumor detection techniques have problems of accuracy, efficiency, and confidentiality. Issues of limited datasets, strict privacy laws that provide restrictions on data sharing, and the necessity for specialized expertise on medical image analysis relegates modern methodologies to vulgar charades. For patient prognosis, treatment planning, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 45–53 Read article
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Fusion of CT and MRI Scanned Medical Images Using Image Processing
Abstract: ABSTRACTIn the field of medicine, to evaluate or to examine the inner body parts, different radiometric scanning techniques can be used. Some most commonly used scanning techniques include the computerized tomography (CT) scan and magnetic resonance imaging (MRI) scan but the images of various body parts taken by using these scanning techniques have their own merits and demerits. MRI scans can show the images of soft tissues very clearly but …
Published in Journal of Computer Technology & Applications · Vol. 3, Issue 3, 2012 · pp. 17–20 Read article
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Detection and Classification of Brain Tumor from MRI And CT Images using Harmony Search Optimization and Deep Learning
Abstract: Primary brain tumor detection and classification are critical factors in ensuring effective treatment and, ultimately, improving patient well-being. This paper describes a novel method for detecting and classifying brain tumors with the help of magnetic resonance imaging (MRI) and computed tomography (CT) images. The suggested method combines harmony search optimization (HSO) and Convolution Neural Networks (CNN) based on deep learning techniques, yielding an impressive accuracy rate of 99.13% for both …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 31–49 Read article
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A Comprehensive Review on Brain Tumour Classification through Deep Learning Utilizing Convolutional Neural Networks
Abstract: Abstract- Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. Brain tumours are classified using a biopsy, which is not normally done before conclusive brain surgery. The enhancement of this technology by machine learning could aid radiologists in tumour detection without the use of …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 12, Issue 3, 2023 · pp. 24–29 Read article
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Uncertainty Prediction in Brain Tumour Segmentation
Abstract: Gliomas are one of the most common brain tumour at different levels of the province, with Magnetic Resonance Imaging (MRI) used for diagnosis. In this project, It was asked to try to find uncertainty in the Brain Tumour Segmentation on MRI images using the BraTs19 Dataset and to look at how machine learning algorithms can work with these MRI images. Since these tissues are so large in shape and appearance, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 13, Issue 2, 2023 · pp. 40–52 Read article
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Neuroimaging in Clinical Trials for Huntington’s Disease: Emerging Research Findings: The Advancement Directions and Implications
Abstract: Neuroimaging is very important in coordinating and conducting Huntington’s disease clinical trials as a tool in selecting patients, managing safety concerns, and assessing the benefits of interventions. This review presents the current uses and potential future uses of structural and functional magnetic resonance imaging (MRI), diffusion imaging, positron emission tomography (PET), proton magnetic resonance spectroscopy (MRS), perfusion imaging, and magneto encephalography (MEG) in HD trials. We describe how these modalities …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 32–44 Read article
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Brain Tumor Detection Using Image Processing
Abstract: Brain tumor means the aggregation of abnormal cells in some tissues of the brain. Brain tumor can be cancerous or noncancerous. The most common types of brain tumors are Glioma, Meningioma and Pituitary tumor. Early detection of tumor cells is essential in-patient treatment and recovery. A brain tumor is typically diagnosed through a lengthy and complicated process. The MRI images of various patients at various stages can be used for …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 2, 2022 · pp. 8–14 Read article
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Posterior Reversible Encephalopathy Syndrome Secondary to Management of Dimorphic Anemia by Repeated Blood Transfusions: A Case Report
Abstract: Posterior reversible encephalopathy syndrome, popularly known asPRES, is defined as a clinicoradiological syndrome which is characterized by symptoms such as headache, seizures, and altered consciousness and characterized by white matter vasogenic edema affecting the posterior occipital and parietal lobes of the brain .We report a 39-year-old woman who was treated by iron and blood transfusion for dimorphic anemia with a hemoglobin (Hb) level of 2 g/dl. Patient developed three episodes …
Published in Research and Reviews: A Journal of Medicine · Vol. 8, Issue 2, 2018 · pp. 6–8 Read article
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Identification and Categorization of Brain Tumors
Abstract: Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Strategic and well-thought-out treatment planning significantly contributes to improving a patient's overall quality of life. Many different imaging techniques, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and also ultrasound are used to evaluate tumors in different parts of the body, with a focus on using MRI images for brain tumors. It is …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 2, 2023 · pp. 32–37 Read article
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U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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Development and Evaluation of Polymer-based Educational Materials for Reducing Anxiety During MRI Scans
Abstract: Magnetic Resonance Imaging (MRI) scans are crucial diagnostic tools, but patient anxiety can hinder successful completion of the procedure. This study investigates the design, development, and evaluation of educational materials fabricated from polymers for use in reducing anxiety in patients undergoing MRI scans. We aimed to create informative and user-friendly materials using polymers due to their potential advantages, such as durability, visual appeal, and ease of disinfection. The educational materials …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 119–124 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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A Review on Detection of Autism Spectrum Disorder Using Signal Processing
Abstract: AbstractAutism is a neural developmental disability associated with impairments in communication and social interaction; it can be detected by various methods such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG). MRI is a technique which captures the image of various sections of brain. It is categorised as structural MRI (sMRI) and functional MRI (fMRI). The detection involves capturing the image, removing the unwanted regions of brain, segmenting the images and …
Published in Current Trends in Signal Processing · Vol. 8, Issue 2, 2018 · pp. 12–24 Read article