A novel lightweight CNN architecture for the diagnosis of brain tumors using MR images
Over the last few years, brain tumor-related clinical cases have increased substantially,
particularly in adults, due to environmental and genetic factors. If they are unidentified in the …
particularly in adults, due to environmental and genetic factors. If they are unidentified in the …
Application of novel DIRF feature selection algorithm for automated brain disease detection
The brain is a complex organ and Magnetic Resonance Imaging (MRI) is the most widely
used imaging modality for diagnosing brain diseases due to its superior soft tissue contrast …
used imaging modality for diagnosing brain diseases due to its superior soft tissue contrast …
Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor
Efficient detection and classification of brain tumors using magnetic resonance images
provide significant support to the neurologists. However, many approaches developed for …
provide significant support to the neurologists. However, many approaches developed for …
Deep Learning Based Segmentation of Brain MRI: Systematic Review (from 2018 to 2022) and Meta-Analysis
P Mahajan, P Kaur - International Journal of Intelligent Systems and …, 2024 - ijisae.org
Background This paper aims to perform an examination and statistical analysis of deep
learning (DL) models utilized in the segmentation of brain tumor MR Images. Methods The …
learning (DL) models utilized in the segmentation of brain tumor MR Images. Methods The …
Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected …
Purpose The purpose of this study is to develop accurate and automated detection and
segmentation methods for brain tumors, given their significant fatality rates, with aggressive …
segmentation methods for brain tumors, given their significant fatality rates, with aggressive …
Uses of artificial intelligence in glioma: A systematic review
A Al‑Rahbi, O Al-Mahrouqi… - Medicine …, 2024 - spandidos-publications.com
Glioma is the most prevalent type of primary brain tumor in adults. The use of artificial
intelligence (AI) in glioma is increasing and has exhibited promising results. The present …
intelligence (AI) in glioma is increasing and has exhibited promising results. The present …
Brain Tumor Segmentation from Multi-Spectral MRI Records Using a U-Net Cascade Architecture
Automated brain tumor classification is an intensively investigated problem, which recently
attracted significant attention. Convolutional neural networks (CNN) and deep learning …
attracted significant attention. Convolutional neural networks (CNN) and deep learning …
A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors
K Sowjanya, KR Reddy… - International Journal of …, 2023 - journal.uob.edu.bh
Brain tumors can develop at any location of the brain with uneven boundaries and shapes.
Typically, they were increasing rapidly due to which its size approximately doubles just in …
Typically, they were increasing rapidly due to which its size approximately doubles just in …
Efficient Deep Learning-Based Brain Tumor Segmentation and Classification Framework Using MRI Images
K Subhashini, J Thangakumar - 2023 7th International …, 2023 - ieeexplore.ieee.org
Brain tumors are generated due to irregular development of brain cells. The life spans of the
tumor-affected individual are complex to predict as they appear in different structures and …
tumor-affected individual are complex to predict as they appear in different structures and …
Using Resizing Layer in U-Net to Improve Memory Efficiency
The segmentation of medical images is becoming increasingly important in the daily clinical
practice, particularly with regard to tumor segmentation. Artificial intelligence, which is …
practice, particularly with regard to tumor segmentation. Artificial intelligence, which is …