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28 articles for “MRI scan”
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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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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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Risk After Pediatric MRI Scanning: A Nation-Wide, Population Based Case-Control Study
Abstract: This paper investigates the potential association between pediatric MRI (Magnetic resonance imaging) exposure and the risk of developing childhood brain tumors, using action-wide, population-based and case-control methodology. The increasing use of MRI in pediatric healthcare has raised concerns about potential long-term health risks, including the risk of developing brain tumors. Detecting the presence or absence of brain tumor through traditional methods might require a lot of time as well as …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 2, 2025 Read article
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Ulcerative Colitis- An Ayurvedic Review And Treatment – A Case Study
Abstract: Ulcerative colitis (UC) is a chronic autoimmune disease that causes inflammation and ulcers of the Large intestine. It is one of the most common GI disorder. Usually the pain affects only the intestinal mucosa and sub mucosa, causing small ulcers in the intestinal lining called ulcers. In most of patients, the disease begins from the anus and continues to spread. Its pathogenesis is multi factorial and includes genetic predisposition, epithelial …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 13, Issue 1, 2024 · pp. 31–39 Read article
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Non -Invasive Ways to Detect Cancer
Abstract: Cancer is a diverse group of diseases characterized by uncontrolled cell growth and division, affecting millions worldwide and being a leading cause of death. The disease typically involves genetic alterations that disrupt the normal balance of cell growth, leading to the formation of tumors. Tumors can be benign, posing no threat as they do not spread, or malignant, capable of invading nearby tissues and metastasizing. There are more than 100 …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 59–67 Read article
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Brain Tumor Detection Using RestNet50 Architecture
Abstract: This paper presents a novel deep learning model for brain tumor diagnosis from MRI scans on the basis of ResNet50 with some modifications. Optimizing the modified layers and pre-trained ResNet50 for improved diagnostic accuracy and reliability in real-world clinical settings is one of the key contributions of this paper. The model was trained on an extremely well-balanced data of 2,577 MRI scans, which were split equally among the tumor and …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 1–13 Read article
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Study on Brain Tumor Detection Using Morphological Operations in MATLAB with Graphical User Interface (GUI)
Abstract: Brain tumor detection plays a crucial role in early diagnosis and effective treatment planning. This research presents a MATLAB-based Graphical User Interface (GUI) for Brain Tumor Detection, incorporating a comprehensive pipeline of image processing techniques. The GUI provides a user-friendly platform, empowering medical professionals to accurately and efficiently analyze MRI brain scans. The GUI begins with text removal to eliminate any textual artifacts that may be present in the MRI …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 24–31 Read article
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Deep Learning-Based Alzheimer’s Disease Detection: A CNN Approach
Abstract: Alzheimer’s disease (AD) is a neurological condition that worsens with time and impairs a patient’s quality of life by causing cognitive loss. For prompt intervention and management of AD, early identification is essential. In this work, we propose a deep learning-based method for automatically classifying Alzheimer’s disease from medical imaging data using convolutional neural networks (CNNs). Our algorithm is intended to evaluate brain MRI images and detect anatomical variations suggestive …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 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 Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection
Abstract: In recent years, deep learning techniques have shown remarkable performance in various image analysis applications, particularly in the domain of medical image processing. Among these, image segmentation plays a critical role, as it helps in isolating and analyzing specific regions within medical images. The proposed study focuses on segmenting the corpus callosum, a vital structure in the human brain, using a novel optimization technique known as the Split and Merge …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification
Abstract: Brain tumors have become a leading cause of cancer- related deaths, posing significant health risks to many patients. This urgent medical challenge calls for rapid, automated, and reliable techniques to detect brain tumors accurately. Timely and precise tumor identification is crucial for devising effective medical plans that have the potential to save lives and improve patient outcomes. By leveraging advanced image processing methods, healthcare professionals can enhance their diagnostic capabilities …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 1, 2026 Read article
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Analysis of White Matter, Gray Matter, and Cerebrospinal Fluid Alterations in Neurological Disorders: A Deep Learning Approach
Abstract: This paper investigates the role of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) alterations in the pathophysiology of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, schizophrenia, and epilepsy. By leveraging advanced deep learning methodologies, we aim to automate the segmentation and analysis of brain structures from MRI scans, enabling a more detailed and precise evaluation of their roles in disease progression. These techniques allow for the identification …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 3, 2024 · pp. 21–27 Read article
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Unresolved and misdiagnosed of floating sensation in young man
Abstract: Persistent Postural-Perceptual Dizziness (PPPD) stands out as a prevalent peripheral vestibular disorder, earning its recognition in the International Classification of Vestibular Disorders in 2017. Characterized by chronicity, this condition manifests symptoms persisting for more than three months, encompassing a disconcerting array such as a floating sensation, unsteadiness, and dizziness. Notably, individuals afflicted with PPPD often report symptom exacerbation following prolonged standing, self-initiated motion, or exposure to dynamic visual stimuli. This …
Published in International Journal of Brain Sciences · Vol. 1, Issue 1, 2024 · pp. 1–4 Read article
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Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson's Disease
Abstract: This paper presents a web-based disease prediction system that integrates machine learning and deep learning techniques to assist in the early detection of Parkinson’s Disease, Diabetes, and Brain Tumors. By utilizing clinical data and MRI images, the platform provides rapid and interpretable predictions to support proactive health management. Logistic Regression models are applied to classify structured datasets for predicting Parkinson’s disease and Diabetes, making use of their effectiveness in binary …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 44–54 Read article
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Late Onset Ataxia: A Case Report
Abstract: 57-year-old man with no prior history of any illness presented with subacute onset of gradually progressive gait ataxia, upper limb incoordination and speech issues. There was history suggestive of orthostatic hypotension and erectile dysfunction. On examination, there was normal cognition with gaze evoked nystagmus, scanning speech and limb incoordination in the form of past pointing, dysdiadochokinesia, and tandem walking abnormality suggestive of pan cerebellar involvement. The patient was evaluated with …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 1, 2024 · pp. 16–20 Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article
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The Early Brain Hemorrhage Prediction System Using Machine Learning
Abstract: Brain hemorrhage is a critical medical emergency that requires immediate attention, as delays in diagnosis can result in severe neurological damage or death. The condition involves bleeding within or around brain tissues, leading to increased intracranial pressure and disruption of normal brain function. Although imaging techniques such as CT scans and MRI provide accurate diagnosis, their availability is limited in emergency and rural settings. In recent years, machine learning has …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 2, 2026 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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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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Horizons in Neuralgia Care: Addressing Unmet Needs and Future Prospects
Abstract: Neuralgia refers to intense, sharp, and often chronic pain resulting from damage or irritation to nerves. It is typically characterized by sudden, shooting pain along the course of a nerve, significantly impairing daily functioning and impacting both physical and emotional well-being. Given its intensity and chronic nature, neuralgia presents a complex challenge in clinical management, emphasizing the importance of timely diagnosis and effective intervention to improve patient outcomes. This comprehensive …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 1–8 Read article