Deep Learning in IoT-Based Healthcare Applications

Zobaed, SM; Hassan, Mehedi; Islam, Muhammad Usama; Haque, Enamul

doi:10.1201/9781003032175-9

Cited by 8 publications

(3 citation statements)

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“…But manually DR identification from fundus images requires higher level of expertise and effort by a specialized ophthalmologist, particularly in remote areas or densely populated counties such as Africa and India [8], wherein the number of people with DR and diabetes can be projected to increase dramatically in the following years, whereas the ophthalmologists are disproportionally low. This has induced the research field for advancing computer-aided diagnosis (CAD) system that minimizes the effort, cost, and time required by a medical professional for identifying DR [9]. New progressions in Artificial Intelligence (AI) and the rise of computation resources and abilities have constituted the opportunity for advancing deep learning applications for precise DR classification and detection [10].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Palaniswamy

Vellingiri

2023

IEEE Access

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Recently, the Internet of Things (IoT) and computer vision technologies find useful in different applications, especially in healthcare. IoT driven healthcare solutions provide intelligent solutions for enabling substantial reduction of expenses and improvisation of healthcare service quality. At the same time, Diabetic Retinopathy (DR) can be described as permanent blindness and eyesight damage because of the diabetic condition in humans. Accurate and early detection of DR could decrease the loss of damage. Computer-Aided Diagnoses (CAD) model based on retinal fundus image is a powerful tool to help experts diagnose DR. Some traditional Machine Learning (ML) based DR diagnoses model has currently existed in this study. The recent developments of Deep Learning (DL) and its considerable achievement over conventional ML algorithms for different applications make it easier to design effectual DR diagnosis model. With this motivation, this paper presents a novel IoT and DL enabled diabetic retinopathy diagnosis model (IoTDL-DRD) using retinal fundus images. The presented Internet of Things Deep Learning -Diabetic Retinopathy Diagnosis (IoTDL-DRD) technique utilizes IoT devices for data collection purposes and then transfers them to the cloud server to process them. Followed by, the retinal fundus images are preprocessed to remove noise and improve contrast level. Next, mayfly optimization based region growing (MFORG) based segmentation technique is utilized to detect lesion regions in the fundus image. Moreover, densely connected network (DenseNet) based feature extractor and Long Short Term Memory (LSTM) based classifier is used for effective DR diagnosis. Furthermore, the parameter optimization of the LSTM method can be carried out by Honey Bee Optimization (HBO) algorithm. For evaluating the improved DR diagnostic outcomes of the IoTDL-DRD technique, a comprehensive set of simulations were carried out. A wide ranging comparison study reported the superior performance of the proposed method. INDEX TERMS Computer aided diagnosis, Deep learning, Diabetic retinopathy, Fundus images, Honey bee optimization.

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Section: Introductionmentioning

confidence: 99%

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Palaniswamy

Vellingiri

2023

IEEE Access

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“…For example, IoT devices can automatically collect a wide range of health metrics, including heart rate, blood pressure, and temperature, from patients who are not physically present in a healthcare facility. This capability eliminates the need for patients to travel to healthcare providers or collect these data themselves [13,14]. Continuous and automatic monitoring of patients offered by IoT devices plays a crucial role in improving healthcare care delivery.…”

Section: Introductionmentioning

confidence: 99%

“…Feasibility studies are carried out to understand to what extent pregnant women can be helped. Machine learning, IoT, and wearables can effectively monitor the health and safety of pregnant women at home and in hospitals [13,16].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review

Hossain,

Kashem,

Islam

et al. 2023

Sensors

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The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

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