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Fully automated 2D and 3D convolutional neural networks pipeline for video segmentation and myocardial infarction detection in echocardiography
(
Springer
, 2022 , Article)
Myocardial infarction (MI) is a life-threatening disorder that occurs due to a prolonged limitation of blood supply to the heart muscles, and which requires an immediate diagnosis to prevent death. To detect MI, cardiologists ...
Federated Learning in NOMA Networks: Convergence, Energy and Fairness-Based Design
(
IEEE
, 2022 , Conference Paper)
Federated Learning (FL) is a collaborative machine learning (ML) approach, where different nodes in a network contribute to learning the model parameters. In addition, FL provides several attractive features such as data ...
Cooperative Machine Learning Techniques for Cloud Intrusion Detection
(
IEEE
, 2021 , Conference Paper)
Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed ...
Convolutional Neural Networks for patient-specific ECG classification
(
IEEE
, 2015 , Conference Paper)
We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and ...
DSPNet: A Self-ONN Model for Robust DSPN Diagnosis From Temperature Maps
(
Institute of Electrical and Electronics Engineers Inc.
, 2023 , Article)
Diabetic sensorimotor polyneuropathy (DSPN) leads to pain, diabetic foot ulceration (DFU), amputation, and death. The diagnosis of advanced DSPN to identify those at risk is key to preventing DFU and amputation. Alterations ...
OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETECTION USING CHEST X-RAY IMAGES
(
IEEE
, 2022 , Conference Paper)
Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets ...
RELIABLE COVID-19 DETECTION USING CHEST X-RAY IMAGES
(
IEEE Computer Society
, 2021 , Conference Paper)
Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest ...
Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies
(
Hindawi Limited
, 2022 , Article)
Background. Diabetic sensorimotor polyneuropathy (DSPN) is a major form of complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is very common ...
Blind ECG Restoration by Operational Cycle-GANs
(
IEEE Computer Society
, 2022 , Article)
Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies ...
Biosignal time-series analysis
(
Elsevier
, 2022 , Book chapter)
In this chapter, recent state-of-the-art techniques in biosignal time-series analysis will be presented. We shall start with the problem of patient-specific ECG beat classification where the objective is to discriminate ...