A Deep-Learning-Based System for Automated Sensing of Chronic Kidney Disease

Bhaskar, Navaneeth; Manikandan, S.

doi:10.1109/lsens.2019.2942145

Cited by 41 publications

(18 citation statements)

References 15 publications

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“…Additionally, the parameters of adaptive learning rate optimization, LR, and Adaptive Moment Estimation (Adam) methods were employed. Bhaskar and Manikandan [ 15 ] presented a novel sensing method for the automatic diagnosis of CKD. The salivary urea concentration is observed for detecting the diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment

Alsuhibany

Abdel‐Khalek

Algarni

et al. 2021

Computational Intelligence and Neuroscience

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Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment

Alsuhibany

Abdel‐Khalek

Algarni

et al. 2021

Computational Intelligence and Neuroscience

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“…( 2020 ) discussed the relationship between the geometrical vascular parameters estimated from the fluorescein angiography (FA) and OCT of the eyes with macular edema. Novel deep learning architectures (Navaneeth and Suchetha 2019 ; Lekha and Suchetha 2017 ; Bhaskar and Manikandan 2019 ; Devarajan et al. 2020 ; Dargan et al.…”

Section: Related Workmentioning

confidence: 99%

“…Qiu and Sun (2019) introduced a self-regulated iterative refinement learning (SIRL) strategy that has a pipeline design to build the exhibition of volumetric image classification in macular OCT. discussed the relationship between the geometrical vascular parameters estimated from the fluorescein angiography (FA) and OCT of the eyes with macular edema. Novel deep learning architectures (Navaneeth and Suchetha 2019;Lekha and Suchetha 2017;Bhaskar and Manikandan 2019;Devarajan et al 2020;Dargan et al 2019) are also used in diverse applications which provide better classification results.…”

mentioning

confidence: 99%

Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN

et al. 2021

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Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.

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“…Qiu et al [28], introduced a self-regulated iterative refinement learning (SIRL) strategy that has a pipeline design to build the exhibition of volumetric image classification in macular optical coherence tomography (OCT). Ajaz et al [29], discussed the relationship between the geometrical vascular parameters estimated from the fluorescein angiography (FA) and optical coherence tomography (OCT) of the eyes with macular edema (ME).Novel deep learning architectures [30][31][32][33][34] Fig. 1 Architecture of CNN are also used in diverse applications which provides better classification results.…”

Section: Related Workmentioning

confidence: 99%

Region of Interest based Predictive Algorithm for Subretinal Hemorrhage Detection using Faster R-CNN

Suchetha

Raman

et al. 2021

Preprint

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Macular Edema (ME) is an essential sort of macular issues caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network (Fast R-CNN) algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of Optical Coherence Tomography (OCT) images of both pre and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the Convolutional Neural Network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.

show abstract

A Deep-Learning-Based System for Automated Sensing of Chronic Kidney Disease

Cited by 41 publications

References 15 publications

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment

Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN

Region of Interest based Predictive Algorithm for Subretinal Hemorrhage Detection using Faster R-CNN

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