Deep learning‐based image reconstruction for different medical imaging modalities
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT)
is a mathematical process that generates images at many different angles around the …
is a mathematical process that generates images at many different angles around the …
[HTML][HTML] Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no
control over tumor growth. Deep learning has been argued to have the potential to …
control over tumor growth. Deep learning has been argued to have the potential to …
[HTML][HTML] Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework
E Irmak - Iranian Journal of Science and Technology …, 2021 - Springer
Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy
specimens today. The current method is invasive, time-consuming and prone to manual …
specimens today. The current method is invasive, time-consuming and prone to manual …
An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis
T Swathi, N Kasiviswanath, AA Rao - Applied Intelligence, 2022 - Springer
Abstract Stock Price Prediction is one of the hot research topics in financial engineering,
influenced by economic, social, and political factors. In the present stock market, the positive …
influenced by economic, social, and political factors. In the present stock market, the positive …
dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI
Glioma is the most prevalent and dangerous type of brain tumor which can be life-
threatening when its grade is high. The early detection of these tumors can improve and …
threatening when its grade is high. The early detection of these tumors can improve and …
[HTML][HTML] API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques
This paper presents API-MalDetect, a new deep learning-based automated framework for
detecting malware attacks in Windows systems. The framework uses an NLP-based encoder …
detecting malware attacks in Windows systems. The framework uses an NLP-based encoder …
[HTML][HTML] Comparing 3D, 2.5 D, and 2D approaches to brain image auto-segmentation
A Avesta, S Hossain, MD Lin, M Aboian, HM Krumholz… - Bioengineering, 2023 - mdpi.com
Deep-learning methods for auto-segmenting brain images either segment one slice of the
image (2D), five consecutive slices of the image (2.5 D), or an entire volume of the image …
image (2D), five consecutive slices of the image (2.5 D), or an entire volume of the image …
[HTML][HTML] An image classification deep-learning algorithm for shrapnel detection from ultrasound images
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced
diagnostics may not be possible. However, image interpretation remains a challenge as …
diagnostics may not be possible. However, image interpretation remains a challenge as …
HANA: a healthy artificial nutrition analysis model during COVID-19 pandemic
Background and objective The impact of diet on COVID-19 patients has been a global
concern since the pandemic began. Choosing different types of food affects peoples' mental …
concern since the pandemic began. Choosing different types of food affects peoples' mental …
Deep learning-based concrete defects classification and detection using semantic segmentation
P Arafin, AHMM Billah, A Issa - Structural Health Monitoring, 2024 - journals.sagepub.com
Visual damage detection of infrastructure using deep learning (DL)-based computational
approaches can facilitate a potential solution to reduce subjectivity yet increase the accuracy …
approaches can facilitate a potential solution to reduce subjectivity yet increase the accuracy …