Advances in Electrical and Computer Technologies
Select Proceedings of ICAECT 2021
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Article
Open AccessAutomated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation
The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper ai...
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Article
Automated retinal disease classification using hybrid transformer model (SViT) using optical coherence tomography images
Optical coherence tomography (OCT) is a widely used imaging technique in ophthalmology for diagnosis and treatment. Recent advances in deep neural networks (DNNs) and vision transformers (ViTs) have paved the ...
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Article
Open AccessDiabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization
Diabetes mellitus (DM) can cause chronic foot issues and severe infections, including Diabetic Foot Ulcers (DFUs) that heal slowly due to insufficient blood flow. A recurrence of these ulcers can lead to 84% o...
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Article
Age- and Severity-Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures
Autism spectrum disorder (ASD) is characterized by divergent etiological factors, comorbidities, severity levels, genetic influences, and functional connectivity (FC) patterns in the brain. In the literature, ...
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Article
PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images
Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model i...
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Chapter
Development of Low Cost, Automated Digital Microscopes Allowing Rapid Whole Slide Imaging for Detecting Malaria
Plasmodium parasites are responsible for the life-threatening illness known as malaria, which remains a significant public health problem across the world, especially in areas with limited access to resources....
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Article
Artificial Intelligence-Based Hearing Loss Detection Using Acoustic Threshold and Speech Perception Level
Hearing loss detection using automated audiometers and artificial intelligence methods has gained increasing attention in recent years. The proposed work aims: (a) to design an automated audiometer to diagnose...
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Article
Automated semantic lung segmentation in chest CT images using deep neural network
Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation u...
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Article
Statistical Reliability Analysis on Flashover Characteristics of Ceramic Disc Insulator and Polymeric Insulators
Composite or polymeric insulators have gained significant attention in recent years compared to conventional ceramic disc insulators used in power transmission. The aim of this study is to investigate the perf...
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Article
NFU-Net: An Automated Framework for the Detection of Neurotrophic Foot Ulcer Using Deep Convolutional Neural Network
Neurotrophic Foot Ulcer (NFU) is most common in patients with diabetes mellitus, and it may result in amputation of the lower extremity (leg and foot). Current methods used for NFU diagnosis are highly complex...
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Chapter
Investigation of the Brain Activation Pattern of Stroke Patients and Healthy Individuals During Happiness and Sadness
This study aimed to assess the emotional experiences of stroke patients and normal people using electroencephalogram (EEG) signals in happiness and sadness. The brain behaviors under both emotional states in t...
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Chapter
Abnormal EEG Detection Using Time-Frequency Images and Convolutional Neural Network
In the process of diagnosing neurological disorders, neurologists often study the brain activity of the patient recorded in the form of an electroencephalogram (EEG). Identifying an abnormal EEG serves as a pr...
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Book and Conference Proceedings
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Article
Recurrent Quantification Analysis-Based Emotion Classification in Stroke Using Electroencephalogram Signals
Stroke is a cerebrovascular disorder, and one of the most common effects of stroke is emotional disturbances. This present work classifies six emotions (anger, sadness, happiness, fear, disgust, and surprise) ...
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Article
Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features
Sudden cardiac arrest (SCA) prediction using electrocardiogram (ECG) and heart rate variability (HRV) signals has received the attention of researchers in recent years. Ventricular fibrillation (VF) is one of ...
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Article
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the br...
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Article
RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks
Early-stage detection of lesions is the best possible way to fight breast cancer, a disease with the highest malignancy ratio among women. Though several methods primarily based on deep learning have been prop...
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Chapter and Conference Paper
ECG Morphological Features Based Sudden Cardiac Arrest (SCA) Prediction Using Nonlinear Classifiers
Sudden Cardiac Arrest (SCA) is a sudden loss of heart function often resulting from an electrical disturbance of the heart that disrupts the blood pum** function. This is mainly due to Ventricular Fibrillati...
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Chapter and Conference Paper
A Study of Non-Gaussian Properties in Emotional EEG in Stroke Using Higher-Order Statistics
The stroke patients often suffered from emotional disturbances, and this leads to perceive emotions differently than normal control subjects; the emotional impairment of the stroke patients can be effectively ...
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Chapter and Conference Paper
Performance Analysis of Wavelet Transform in the Removal of Baseline Wandering from ECG Signals in Children with Autism Spectrum Disorder (ASD)
The electrocardiogram (ECG) signals are used for prediction of various cardiovascular diseases and also as a prominent signal for develo** intelligent healthcare and wearable systems. The ECG signal is mostl...
