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89 articles for “MRI image”
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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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Brain Tumor Detection Using an Artificial Neural Network in MRI Images
Abstract: MRI is the widely used imaging technique in the biomedical field for the detection, diagnosis and evaluation of brain tumors. The large structural variability among brain tumor makes detection of a challenging problem. The medical problems are severe, if the tumor is detected at the later stage. Hence, diagnosis is necessary at the earliest. In this work, a neural network classifier is used for the detection of tumors from the …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 5, Issue 1, 2018 · pp. 14–22 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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Numerical Simulation and Design of Efficient Brain Tumor Segmentation with Hybrid Fuzzy K-Means Clustering Technique
Abstract: The goal of this study is to detect brain cancers from MRI images using a Matlab GUI interface. Using the GUI, this application may employ numerous combinations of segmentation, filters, and other image processing methods to produce the best results. We begin by applying the Prewitt horizontal edge-emphasizing filter on the image. “Watershed pixels” are the next step in detecting tumours. The most essential aspect of this project is that …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 3, 2021 · pp. 8–17 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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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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A Review of Various Deep Learning Models for Classification of BrainTumor
Abstract: Brain Tumor is most challenging and life threading decease now these days. Timely accurately detection of brain tumor is play important role for patient health improvement and it increase life survival rate. Traditional of brain tumor detection relay heavily on manual interpretation of medicalimage, it is very time consuming and chance to human error in detection of brain tumor. Now in recent advancement of Artificial intelligence (AI), Machine learning (ML) …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 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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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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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article
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Evaluation of Small Vessel Disease by Advanced Brain Imaging
Abstract: Studying and comprehending brain small vessel disease requires extensive imaging. Recent applications of cutting-edge brain imaging techniques have led to the discovery of several significant results. Diffusion-weighted MRI studies have demonstrated the diagnostic accuracy of using clinical features alone or in combination with CT scan results to identify small vessel disease as the underlying cause is suboptimal in patients with acute lacunar syndromes. Acute infarcts caused by small vessel disease …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 15–19 Read article
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Brain Tumor Detection
Abstract: The MRI, or magnetic resonance imaging, is one of the most common practices in detecting a brain tumor since it contains important information used to extensively scan the human brain’s internal anatomy. The treatment of any tumor depends entirely on the knowledge and expertise of the physician. This paper reflects the identification of brain tumors using different machine learning algorithms along with the convolution neural network (CNN) and transfer learning. …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 8, Issue 1, 2021 · pp. 45–52 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 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