Machine Learning and Internet of Things Techniques to Assist the Type I Diabetic Patients to Predict the Regular Optimal Insulin Dosage

Jebaseeli, T. Jemima; David, D. Jasmine; Jegathesan, V.

doi:10.1007/978-3-030-63937-2_9

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Therefore, regular eye screening is mandated for patients affected by diabetes, as prompt diagnosis of the condition becomes necessary [5]. Though earlier identification of DR depends upon operational changes in Electro Retino-Graphy (ERG), retinal blood vessel caliber, and retinal blood flow, in medical practices initial identification depends on fundus analysis [6]. Fundus photography was a well-tolerated, rapid, non-invasive, and broadly accessible that forms one method that is mostly utilized for assessing the extent of DR [7].…”

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%