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

Emin Argun Oral

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 223, С. 119790 - 119790

Опубликована: Март 14, 2023

Язык: Английский

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

Fakhri Karray

и другие.

Information Fusion, Год журнала: 2021, Номер 76, С. 355 - 375

Опубликована: Июль 5, 2021

Язык: Английский

Процитировано

207

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

Ujwal Bhatia,

N. Z. Jhanjhi

и другие.

Multimedia Systems, Год журнала: 2021, Номер 29(3), С. 1729 - 1738

Опубликована: Апрель 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.

Язык: Английский

Процитировано

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

и другие.

Information Fusion, Год журнала: 2021, Номер 76, С. 376 - 421

Опубликована: Июль 10, 2021

Язык: Английский

Процитировано

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

и другие.

Cluster Computing, Год журнала: 2022, Номер 26(4), С. 2271 - 2311

Опубликована: Авг. 17, 2022

Язык: Английский

Процитировано

176

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

и другие.

Biomedical Signal Processing and Control, Год журнала: 2021, Номер 69, С. 102826 - 102826

Опубликована: Июнь 3, 2021

Язык: Английский

Процитировано

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, Год журнала: 2022, Номер 36(1), С. 111 - 131

Опубликована: Апрель 19, 2022

Язык: Английский

Процитировано

85

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

и другие.

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

Процитировано

58

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

­ Mamta

и другие.

Information Fusion, Год журнала: 2024, Номер 110, С. 102472 - 102472

Опубликована: Май 16, 2024

Язык: Английский

Процитировано

33

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

Nonita Sharma,

Monika Mangla, Sachi Nandan Mohanty

и другие.

Biomedical Signal Processing and Control, Год журнала: 2021, Номер 68, С. 102717 - 102717

Опубликована: Май 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.

Язык: Английский

Процитировано

100

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

и другие.

Computers in Biology and Medicine, Год журнала: 2021, Номер 136, С. 104742 - 104742

Опубликована: Авг. 8, 2021

Язык: Английский

Процитировано

73