A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Gunasekaran, K.; Pitchai, R.; Chaitanya, G. Krishna; Selvaraj, D.; Sheryl, S. Annie; Almoallim, Hesham S.; Alharbi, Sulaiman Ali; Raghavan, S. S.; Tesemma, Belachew Girma

doi:10.1155/2022/3163496

Cited by 22 publications

(9 citation statements)

References 44 publications

(46 reference statements)

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“…In this study, optimization was done for a particular type of neural network which could classify the images and predict diabetic retinopathy. Examples like these validate the promise of technology in improving the speed and accuracy of detecting various diseases 37 . Technology such as deep learning can predict the disease early and make an interconnection between the two conditions.…”

Section: A2 Accuracymentioning

confidence: 64%

Novel 4A Framework for Measuring Outcomes of Technological Implementation in The Healthcare Industry

Rajaram

Pandey

Saproo³

et al. 2023

Int J Life Sci Pharm Res

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The healthcare industry is ever-evolving, and advancements in this industry are happening on multiple fronts, i.e. scientific, medical, regulatory etc. This industry has witnessed significant changes concerning technology usage, which has changed the overall complexion. The advent of advanced computing tools, such as AI, ML etc., has significantly augmented the capabilities within the industry and is making this industry future ready. The healthcare industry has been reactive, i.e. it treats individuals after they get the disease; however, with the application of the latest tools, the healthcare industry is becoming more predictive, i.e. it can predict the risk profile. This is the most significant impact advanced tools have brought to this industry. Advanced computing tools are being applied to multiple functions across the industry, such as drug development, patient treatment, diagnosis, insurance reimbursement etc. As there are multitudes of applications projecting different kinds of benefits, it becomes essential that we have a tool or framework which can measure the impact. This study aimed to develop a framework to evaluate the benefits of implementing these advanced computing tools. The primary objective of this technology application should be to impact the primary outcomes, i.e. accessibility, speed and accuracy, patient compliance and affordability. We must have a framework that objectively assesses the impact and provides guidance to all the stakeholders. This paper proposes a novel 4A framework to help all stakeholders assess the effects. The central idea in healthcare is to improve a patient's life. One can do that by making medicines more productive, reducing adverse effects, improving predictability, reducing cost, improving access etc. This paper provides a strategic framework to assess the impact on the suitable parameters. Novel 4A framework measures the effect on these critical parameters, i.e. access, accuracy, affordability and adherence. As this industry touches patient's life, there are multiple concerns and the primary amongst them, especially for the technology applications, are the ones related to legal and ethical. The overall archetype proposed in this research paper takes into account and provides a framework for the impact assessment. The novel 4A framework presented in this article will serve as a tool to evaluate the impact advanced computing tools will have on the healthcare industry.

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Section: A2 Accuracymentioning

confidence: 64%

Novel 4A Framework for Measuring Outcomes of Technological Implementation in The Healthcare Industry

Rajaram

Pandey

Saproo³

et al. 2023

Int J Life Sci Pharm Res

View full text Add to dashboard Cite

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“…For example, the study [8] found that an AI-based DR diagnosis system can achieve an accuracy of 90.6% in a real-world setting. However, in [9], research found that the accuracy of an AIbased DR diagnosis system dropped to 85%. Various factors can influence the accuracy of these systems, such as image quality, concurrent eye diseases, and the stage of DR itself.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

Ahnaf Alavee,

Hasan,

Hasnayen Zillanee

et al. 2024

IEEE Access

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Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for singlelabel (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We present insights into their respective accuracy and the challenges they face. We anticipate that these insights will prove valuable to researchers and practitioners in the field. Our ambition is that this in-depth study and comparison of models will inform and inspire future research endeavors, ultimately leading to enhanced disease detection in medical imaging and thereby assisting healthcare professionals.

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“…Gunasekaran et al [15] use RNN for the purpose of retrieving features from deep networks. So, utilizing computational techniques for identifying some ailments automatically becomes effective solution.…”

Section: Literature Reviewmentioning

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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A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Cited by 22 publications

References 44 publications

Novel 4A Framework for Measuring Outcomes of Technological Implementation in The Healthcare Industry

Novel 4A Framework for Measuring Outcomes of Technological Implementation in The Healthcare Industry

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

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

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