MMHFNet: Multi-modal and multi-layer hybrid fusion network for voice pathology detection DOI
Hussein M.A. Mohammed, Aslı Nur Ömeroğlu,

Emin Argun Oral

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 223, P. 119790 - 119790

Published: March 14, 2023

Language: Английский

A comprehensive survey on multimodal medical signals fusion for smart healthcare systems DOI
Ghulam Muhammad, Fatima Alshehri,

Fakhri Karray

et al.

Information Fusion, Journal Year: 2021, Volume and Issue: 76, P. 355 - 375

Published: July 5, 2021

Language: Английский

Citations

207
Medical image-based detection of COVID-19 using Deep Convolution Neural Networks DOI Creative Commons
Loveleen Gaur,

Ujwal Bhatia,

N. Z. Jhanjhi

et al.

Multimedia Systems, Journal Year: 2021, Volume and Issue: 29(3), P. 1729 - 1738

Published: April 28, 2021

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. exponential rise in cases burdens healthcare facilities, and a vast amount multimedia data being explored to find solution. This study presents practical solution detect from chest X-rays while distinguishing those normal impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, InceptionV3) are evaluated through transfer learning. rationale selecting these specific their balance accuracy efficiency with fewer parameters suitable mobile applications. dataset used publicly available compiled different sources. uses deep learning techniques performance metrics (accuracy, recall, specificity, precision, F1 scores). results show that proposed approach produced high-quality model, an overall 92.93%, COVID-19, sensitivity 94.79%. work indicates definite possibility implement computer vision design enable effective screening measures.

Language: Английский

Citations

190
Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects DOI Creative Commons
Shuihua Wang‎, M. Emre Celebi, Yudong Zhang

et al.

Information Fusion, Journal Year: 2021, Volume and Issue: 76, P. 376 - 421

Published: July 10, 2021

Language: Английский

Citations

185
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues DOI Open Access
Anichur Rahman, Md. Sazzad Hossain, Ghulam Muhammad

et al.

Cluster Computing, Journal Year: 2022, Volume and Issue: 26(4), P. 2271 - 2311

Published: Aug. 17, 2022

Language: Английский

Citations

176
Electroencephalography-based motor imagery classification using temporal convolutional network fusion DOI
Yazeed K. Musallam, Nasser I. AlFassam, Ghulam Muhammad

et al.

Biomedical Signal Processing and Control, Journal Year: 2021, Volume and Issue: 69, P. 102826 - 102826

Published: June 3, 2021

Language: Английский

Citations

129
Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence DOI Open Access

Saad I. Nafisah,

Ghulam Muhammad

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 36(1), P. 111 - 131

Published: April 19, 2022

Language: Английский

Citations

85
Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

58
Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare DOI
Niyaz Ahmad Wani, Ravinder Kumar,

­ Mamta

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: 110, P. 102472 - 102472

Published: May 16, 2024

Language: Английский

Citations

33
A smart ontology-based IoT framework for remote patient monitoring DOI Creative Commons

Nonita Sharma,

Monika Mangla, Sachi Nandan Mohanty

et al.

Biomedical Signal Processing and Control, Journal Year: 2021, Volume and Issue: 68, P. 102717 - 102717

Published: May 18, 2021

The Internet of Things (IoT) is the most promising technology in health systems. IoT-based systems ensure continuous monitoring indoor and outdoor settings. Remote has revolutionized healthcare by connecting remote hard-to-reach regions. Specifically, during this COVID-19 pandemic, it imperative to have a system assess patients remotely curb its spread prematurely. This paper proposes framework that provides updated information Corona Patients vicinity thus identifiable data for locality cohorts. proposed model access an alarm-enabled bio wearable sensor early detection based on ontology method using sensory 1D Biomedical Signals such as ECG, PPG, temperature, accelerometer. ontology-based analyzes challenges encompassing security privacy issues. also simulated cooza simulator. During simulation, observed achieves accuracy 96.33 %, which establishes efficacy model. effectiveness strengthened efficient power consumption.

Language: Английский

Citations

100
Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification DOI Open Access
Marco La Salvia, Gianmarco Secco, Emanuele Torti

et al.

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 136, P. 104742 - 104742

Published: Aug. 8, 2021

Language: Английский

Citations

73