medical image segmentation plays a crucial role in identifying and analyzing anatomical structures in medical images. This requires an accurate medical segmentation tool to delineate and quantitatively analyze the tar...
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Fifteen novices completed a Zentangle task while their brain activity was measured with electroencephalography (EEG) and their concentration emotional state, stress, and anxiety levels were evaluated with questionnair...
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This study aims to reduce the risk of aerosol infection in patients and healthcare workers by establishing a portable negative pressure filtering chest drainage system (PNPFCdS) that filters aerosols and microparticle...
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Advancements in high-frequency communication devices have emphasized miniaturization and low energy consumption. This work focuses on developing microwave dielectric materials characterized by an ultra-low dielectric ...
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developing cost-effective photoelectrodes with a low band gap in the NIR-visible regions remains a challenge to achieve effective hydrogen production. We report the details of superstrate-configuredphotocathodes, con...
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In brain tumor research, the use of machine learning based methods using MRI data to locate brain tumors accurately has significantly increased and new techniques are proposed to facilitate the medical experts. There ...
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ISBN:
(数字)9798350386844
ISBN:
(纸本)9798350386851
In brain tumor research, the use of machine learning based methods using MRI data to locate brain tumors accurately has significantly increased and new techniques are proposed to facilitate the medical experts. There is still room for improvement as tumors have complex anatomy which requires efforts to explore further and propose new deep learning based solutions. In this research, we propose 3d UNet based multimodal brain tumor segmentation for Non Enhancing Tumor (NET), Peritumoral Edema (PE), and Enhancing Tumor (ET). data preprocessing and augmentation techniques are employed to optimise the model's performance. The dataset used in this study to train, validate, and test the model performance is the BraTS 2020 dataset. The model achieved improved accuracy and IoU after implementing the mentioned techniques. Results before and after data augmentation demonstrate a significant enhancement in model performance. The model achieved an improved mIoU of 0.77 and accuracies of 98.21% and 99.36% for actual data and augmenteddata for three brain tumor classes, i.e., NET, PE, and ET. Our model performed multiclass brain tumor segmentation with high accuracies, and we aim to enhance this study to propose a valuable medical assistance model.
medical image segmentation plays a crucial role in identifying and analyzing anatomical structures in medical images. This requires an accurate medical segmentation tool to delineate and quantitatively analyze the tar...
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ISBN:
(数字)9798350394924
ISBN:
(纸本)9798350394931
medical image segmentation plays a crucial role in identifying and analyzing anatomical structures in medical images. This requires an accurate medical segmentation tool to delineate and quantitatively analyze the target regions, diagnose any abnormality, and assist in treatment planning. deep learning approaches have emerged as a promising solution for automating medical segmentation. However, challenges arise when dealing with the complex shapes and spatial variations of some target regions, especially in 3d MRI scans. To deal with such transformations, specific techniques are required to properly analyze and preprocess the dataset and perform image filtering to provide better features for improved prediction performance of deep learning architectures. This study focuses on improving brain tumor segmentation in multimodal 3d MRI images. We observed significant improvements in multimodal brain tumor segmentation results (accuracy, IoU, and mIoU) using an optimized 3d Gabor filter, which helps extract meaningful features. Multichannel input images were preprocessed to remove noise and select an appropriate resolution to reduce computational complexity. An improvement in mean Intersection over Union (mIoU) from 0.714 to 0.804 and accuracy from 0.982 to 0.991 were achieved, which shows a major improvement. This work contributes to the field of medical image segmentation by offering an improved and efficient approach for brain tumor analysis in 3d MRI scans, potentially aiding in diagnosis and treatment planning.
This study combines 3d printing, anodic oxidation (AO), and electrophoretic deposition (Ed) technologies to develop Ti-6Al-4V alloy (abbreviated as Ti64) with high hydrophilicity, adhesion, and bioactivity. Titanium o...
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Sensors in recent days have been in great demand in industrial zones of simple, low-cost sensors for effectively sensing various gases. This research work develops electrospinning fibers for optical ammonia sensing ba...
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In brain research, brain tissue segmentation techniques have offered vast aid and possibilities for quantitative analysis of the brain to detect brain tumor and other brain issues. In this paper, UNet-based multiclass...
In brain research, brain tissue segmentation techniques have offered vast aid and possibilities for quantitative analysis of the brain to detect brain tumor and other brain issues. In this paper, UNet-based multiclass brain tissue segmentation is presented. To achieve the best performance of the model, data augmentation techniques are applied. The training, validation and testing of the model is performed on the BrainWeb dataset. The model has achieved improved accuracy and IoU after data augmentation. Both pre and post data augmentation results are presented which show clear improvement in model performance after data augmentation. The achieved IoU for white matter, gray matter and cerebrospinal fluid are 0.84, 0.95 and 0.92, respectively. Our model accurately performed the multiclass brain tissue segmentation and this study proposes a valuable medical aid model.
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