N. Sarshar
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View article: The role of data augmentation and attention mechanisms in UNet and ConvNeXt architectures for optimizing breast tumor segmentation
The role of data augmentation and attention mechanisms in UNet and ConvNeXt architectures for optimizing breast tumor segmentation Open
This study conducts a comprehensive analysis of various configurations of the UNet + ConvNeXt Tiny architecture for breast tumor segmentation. We assess the influence of data augmentation, skip connections, attention mechanisms, and dropou…
View article: Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method
Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method Open
Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural N…
View article: Transformer-Based Multi-Modal Deep-Learning for Enrichment-Free Highly Sensitive Detection and Isolation of Circulating Plasma Cells in Multiple Myeloma
Transformer-Based Multi-Modal Deep-Learning for Enrichment-Free Highly Sensitive Detection and Isolation of Circulating Plasma Cells in Multiple Myeloma Open
Circulating plasma cells (CPCs) in Multiple Myeloma (MM) patients are promising non-invasive biomarkers for risk stratification, treatment monitoring, and prognostication. High CPC levels correlate with poorer progression-free survival (PF…
View article: Advancing Brain MRI Images Classification: Integrating VGG16 and ResNet50 with a Multi-verse Optimization Method
Advancing Brain MRI Images Classification: Integrating VGG16 and ResNet50 with a Multi-verse Optimization Method Open
This research presents a novel methodology for classifying MRI images into two categories: tumor and non-tumor. The study utilizes a combination of two advanced Convolutional Neural Network (CNN) architectures, VGG16 and ResNet50, to addre…
View article: Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network
Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network Open
In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of…
View article: ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition
ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition Open
Breast tumor segmentation and recognition from mammograms play a key role in healthcare and treatment services. As different tumors in mammography have dissimilar densities, shapes, sizes, and edges, the interpretation of mammograms can be…
View article: A Deep Learning Approach for Robust, Multi-oriented, and Curved Text Detection
A Deep Learning Approach for Robust, Multi-oriented, and Curved Text Detection Open
Automatic text localization and segmentation in a normal environment with vertical or curved texts are core elements of numerous tasks comprising the identification of vehicles and self-driving cars, and preparing significant information f…
View article: Review of Deep Learning Approaches for Thyroid Cancer Diagnosis
Review of Deep Learning Approaches for Thyroid Cancer Diagnosis Open
Thyroid nodule is one of the common life-threatening diseases, and it had an increasing trend over the last years. Ultrasound imaging is a commonly used diagnostic method for detecting and characterizing thyroid nodules. However, assessing…
View article: Premature Ventricular Contraction Recognition Based on a Deep Learning Approach
Premature Ventricular Contraction Recognition Based on a Deep Learning Approach Open
Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a pr…
View article: Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network
Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network Open
One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in th…
View article: Nerve optic segmentation in CT images using a deep learning model and a texture descriptor
Nerve optic segmentation in CT images using a deep learning model and a texture descriptor Open
The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-co…
View article: Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition
Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition Open
Today, cardiovascular disease has become an epidemic. Statistics show that one person dies every 33 seconds due to cardiovascular disease. It is estimated that 33% of men and 10% of women have a heart attack before the age of 60. Arrhythmi…