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65 articles for “magnetic resonance image”
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Enhancement of Proton Density Weighted Magnetic Resonance Images using Singular Value Decomposition in Wavelet Domain
Abstract: Image enhancement techniques for low contrast medical images using Singular Value Decomposition (SVD) in Discrete Wavelet Transform domain (SVD-DWT) are proposed in the literature. However, shift sensitivity, poor directionality and a lack of phase information are the primary drawbacks of the discrete wavelet transform. This work introduces a novel method of image enhancement (SVD-SWT) using SVD in Stationary Wavelet Transform (SWT) to improve the contrast of Proton Density weighted Magnetic …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 10, Issue 2, 2022 · pp. 10–21 Read article
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Automatic Detection and Classification of Brain Tumor in Magnetic Resonance Images
Abstract: Brain tumor is one of the serious diseases that have caused death to many people in recent years. The complex structure of the brain and its main function associated with the central nervous system and its critical role in controlling most of the functions of the body make detection of tumor a challenging task. Many techniques have been presented in the medical field in order to detect brain tumor from …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 5, Issue 2, 2018 · pp. 34–40 Read article
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Methods for Identifying Regions of Brain Establishment Using Functional Magnetic Resonance Imaging
Abstract: AbstractPurposeful neuro imaging could be a comparatively young discipline inside the neurosciences that has led to important advances in our understanding of the human brain and progress in neuro research project associated with public health. Accurately distinctive activated regions within the brain showing a powerful association with associate in nursing outcome of interest is crucial in terms of sickness prediction and interference. Purposeful resonance imaging (fMRI) is the most generally …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 4, Issue 1, 2017 · pp. 12–15 Read article
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Convolutional Neural Network and Transfer Learning-based Approach for Brain Tumor Detection in Magnetic Resonance Imaging
Abstract: Brain tumors are among the most invasive illnesses that can affect both children and adults. Brain tumors develop very quickly, and if not treated at the proper time, they decrease the patient's chances of survival. It is crucial to find brain tumors at an early stage. To increase patients’ life expectancy, proper treatment planning and precise diagnostics are most important. The best way to detect brain tumors is via Magnetic …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 13, Issue 2, 2023 · pp. 23–30 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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Clinicoradiological Evaluation of Idiopathic Intracranial Hypertension
Abstract: Idiopathic intracranial hypertension refers to elevated intracranial pressure when there is no detectable space-occupying lesion. Individuals with idiopathic intracranial hypertension commonly exhibit classic indications of increased intracranial pressure, including headaches, nausea, neck discomfort, double vision, temporary visual obscurity, and swelling of the optic nerve head (papilledema). Typical magnetic resonance imaging findings include empty sella, optic nerve tortuousity, globe flattening, and transverse sinus stenosis. All patients from July 2021 to June …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 13, Issue 2, 2023 · pp. 40–45 Read article
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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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Amalgamation of Medical Image using Wavelet Theory
Abstract: Image fusion has become a common term used within medical diagnostics and treatment. The term is used when multiple patient images are registered and overlaid or merged to provide additional information. Fused images may be created from multiple images from the same imaging modality, or by combining information from multiple modalities, such as magnetic resonance image (MRI), computed tomography (CT) etc. In radiology and radiation these images serve different purposes. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 2, Issue 1, 2015 · pp. 9–14 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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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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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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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 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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Detection and Classification of Alzheimer’s Disease Using Deep Learning Technique
Abstract: It is crucial that people with Alzheimer's disease (AD) receive a proper diagnosis to begin preventative action before irreparable brain damage develops. Most people who suffer from Alzheimer's disease (AD), a neurological condition that progresses, are older than 65. The area of interest (ROI) in the hippocampus has been extensively studied for several purposes, including neurological illness research, stress development monitoring, and memory function analysis. Moreover, a connection between Alzheimer's …
Published in Recent Trends in Sensor Research & Technology · Vol. 12, Issue 1, 2025 · pp. 15–20 Read article
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Comparative Analysis and Future Research Directions in AI in Healthcare: Medical Imaging and Diagnostics
Abstract: Artificial intelligence (AI) is reshaping healthcare, particularly in the areas of medical imaging and diagnostic practice. By using advanced techniques like machine learning and deep learning, AI systems help improve the accuracy, speed, and effectiveness of identifying diseases and analyzing medical images. This paper provides a comprehensive overview of the application of artificial intelligence in medical imaging and highlights its growing importance in clinical diagnostics. It discusses how AI-based systems …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 8–13 Read article
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Imaging Technologies in Breast Cancer Screening Beyond Mammography
Abstract: There is a passionate debate in medical world about the best screening method for breast cancer. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the most widely used screening modality, with solid evidence of benefit for women aged 40 to 74 years. Even then it has also undergone increased scrutiny for false-positives with additional testing which increase radiation dose, cost and anxiety. False-negatives with …
Published in Journal of Advances in Shell Programming · Vol. 7, Issue 1, 2020 · pp. 22–32 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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A Comparative Study of different Techniques to predict Maternal Morbidity and Mortality Model
Abstract: Artificial intelligence (AI) encompasses a range of techniques, including machine learning and deep learning, which are increasingly utilized in the healthcare sector for tasks such as disease diagnosis and drug discovery. To achieve accurate disease diagnosis through AI, it is essential to integrate data from multiple medical sources, including ultrasound imaging, magnetic resonance imaging (MRI), mammography, genomics, and computed tomography (CT) scans, among others. This article presents a comprehensive review …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 1, 2025 Read article
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Survey of Liver Disease Prediction Using Machine Learning
Abstract: Liver disease is one of the major causes of death in all over world that impacts the number of people around the world. Diagnosis of liver at early stages is crucial to reduce morality. As technology is growing in health care, machine learning significantly affects health care for predicting conditions at early stages. This study reviews the different approaches based on machine learning and deep learning to predict liver disease. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 1, 2023 · pp. 30–38 Read article
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Comparative Analysis of Proposed FCM Clustering Integrated Enhanced Firefly-Optimized Algorithm (En-FAOFCM) for MR Image Segmentation and Performance Evaluation
Abstract: Image segmentation has a significant responsibility in diagnosis and treatment of diseases. To scan patients and determine the severity of certain injuries in hospitals, magnetic resonance imaging (MRI) method is normally used. This paper emphasizes on comparative study of segmentation techniques for segmenting MRI brain images. In this regard, to group the pixels of images in the intensity space, unsupervised clustering techniques are used. Here enhanced firefly algorithm is used …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 1, 2016 · pp. 32–44 Read article