1
|
Hadi MU, Qureshi R, Ahmed A, Iftikhar N. A lightweight CORONA-NET for COVID-19 detection in X-ray images. Expert Syst Appl 2023; 225:120023. [PMID: 37063778 PMCID: PMC10088342 DOI: 10.1016/j.eswa.2023.120023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors.
Collapse
Affiliation(s)
- Muhammad Usman Hadi
- Nanotechnology and Integrated Bio-Engineering Centre (NIBEC), School of Engineering, Ulster University, BT15 1AP Belfast, UK
| | - Rizwan Qureshi
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX 77030, USA
| | - Ayesha Ahmed
- Department of Radiology, Aalborg University Hospital, Aalborg 9000, Denmark
| | - Nadeem Iftikhar
- University College of Northern Denmark, Aalborg 9200, Denmark
| |
Collapse
|
2
|
Qureshi R, Irfan M, Gondal TM, Khan S, Wu J, Hadi MU, Heymach J, Le X, Yan H, Alam T. AI in drug discovery and its clinical relevance. Heliyon 2023; 9:e17575. [PMID: 37396052 PMCID: PMC10302550 DOI: 10.1016/j.heliyon.2023.e17575] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/21/2023] [Indexed: 07/04/2023] Open
Abstract
The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.
Collapse
Affiliation(s)
- Rizwan Qureshi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, USA
| | - Muhammad Irfan
- Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Swabi, Pakistan
| | | | - Sheheryar Khan
- School of Professional Education & Executive Development, The Hong Kong Polytechnic University, Hong Kong
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, USA
| | | | - John Heymach
- Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas, MD Anderson Cancer Center, Houston, USA
| | - Xiuning Le
- Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas, MD Anderson Cancer Center, Houston, USA
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| |
Collapse
|
3
|
Heaney J, Buick J, Hadi MU, Soin N. Internet of Things-Based ECG and Vitals Healthcare Monitoring System. Micromachines (Basel) 2022; 13:2153. [PMID: 36557452 PMCID: PMC9780965 DOI: 10.3390/mi13122153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient's heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient's physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient's status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.
Collapse
|