Acceptance of Telemedicine and E-Health Applications in Developing Countries

Mamoun, Ragda; Nasor, Mohamed; Abulikailik, Sahar H.

doi:10.1109/iccceee49695.2021.9429558

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Mamoun et al [ 35 ] developed a healthcare mobile application prototype. The application provides diseases to be diagnosed online by medical specialists.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning‐Based Skin Diseases Classification using Smartphones

Oztel,

Yolcu Oztel,

Sahin

2023

Advanced Intelligent Systems

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Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms.

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“…Mamoun et al [ 35 ] developed a healthcare mobile application prototype. The application provides diseases to be diagnosed online by medical specialists.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning‐Based Skin Diseases Classification using Smartphones

Oztel,

Yolcu Oztel,

Sahin

2023

Advanced Intelligent Systems

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show abstract

“…The resulting dependences can be described quite accurately by straight lines. n a n v i = ) ( (7) where ɑ i − is the corresponding slope ratio.…”

Section: Simulation Modelmentioning

confidence: 99%

“…This is a field of knowledge that studies the tasks of digitalization of medical service delivery, development of new high-precision medical equipment, integration of medical systems with telecommunication technologies, implementation and application of robots and artificial intelligence (AI) in medicine [2,3]. Today most countries in one way or another implement e-health system in everyday medical practice [4,5,6], the level of trust of patients and doctors in such systems is steadily increasing [7].…”

Section: Introductionmentioning

confidence: 99%

Prototype Design and Experimental Evaluation e-Healthcare System based on Molecular Analysis Devices

Zakharov¹,

Paramonov²,

Muthanna³

et al. 2021

IJACSA

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Recently, wireless body area networks (WBANs) and mobile Internet of Things (IoT) have been greatly increased and integrated into a different type of systems such as electronic/mobile-healthcare (e/m-healthcare) systems. In addition, analyzing the composition of drugs or performing the medical rapid tests in the conditions field are the main tasks of the m-healthcare system, in which the traditional laboratory methods of analysis are not suitable. Therefore, in this study, we proposed a novel structure with a distributed e-health system to perform such analysis, where portable infrared micro spectrometers are utilized and then boundary calculations are applied. More specifically, this system is proposed to use a portable infrared micro spectrometer with a specially designed application connected to a public communication network, which can process the results of analysis using boundary calculations. Moreover, it provides remote processing and long-term storage of analysis data using artificial neural networks and cloud technologies. Finally, simulation results show that preprocessing (error checking), data buffering and Edge Computing can significantly reduce the network latency and volume of transferred data.

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Unleashing potential: a deep dive into AI-blockchain integration for UAV-enhanced tele-surgery

S.,

K. S.

2024

Cogent Engineering

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Acceptance of Telemedicine and E-Health Applications in Developing Countries

Cited by 6 publications

References 2 publications

Deep Learning‐Based Skin Diseases Classification using Smartphones

Deep Learning‐Based Skin Diseases Classification using Smartphones

Prototype Design and Experimental Evaluation e-Healthcare System based on Molecular Analysis Devices

Unleashing potential: a deep dive into AI-blockchain integration for UAV-enhanced tele-surgery

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