Diabetic Retinopathy Detection Using Convolutional Neural Network—A Study

Wahid, Farha Fatina; Raju, G.

doi:10.1007/978-981-15-5309-7_13

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…The model achieved an accuracy of 86.64%, a loss of 0.46, and an average F1 score of 0.6318 across all five different stages of DR. Furthermore, other researchers [ 44 , 45 ] reviewed the contributions of numerous studies in the field of DR detection and classification, highlighting the implementation of both ML and DL models in these endeavors.…”

Section: Related Studiesmentioning

confidence: 99%

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Mohanty

Mahapatra²,

Acharya

et al. 2023

Sensors

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Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

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Section: Related Studiesmentioning

confidence: 99%

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Mohanty

Mahapatra²,

Acharya

et al. 2023

Sensors

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“…Zhang [24] Deep transfer learning to classify Parkinson's disease patients from healthy subjects based on sEMG signals with high accuracy and robustness Proposed a novel method that uses deep transfer learning to classify Parkinson's disease patients from healthy subjects based on sEMG signals with high accuracy and robustness Used a small dataset of 30 subjects; did not validate the method on other neurological disorders or compare it with other transfer learning methods R. Byfield, M. Guess, and J. Lin [25] Machine learning framework that can estimate the full 3-D lower-body kinematics and kinetics of patients with knee osteoarthritis from sEMG signals with high accuracy and reliability Developed a machine learning framework that can estimate the full 3-D lower-body kinematics and kinetics of patients with knee osteoarthritis from sEMG signals with high accuracy and reliability Used a small dataset of 10 subjects; did not test the framework on other gait conditions or evaluate its clinical relevance or applicability D. Buongiorno, G. D. Cascarano, and V. Bevilacqua [26] Comprehensive overview of the current state-of-the-art methods and challenges in processing sEMG signals using deep learning techniques, with a focus on the taxonomy, applications, and open issues Provided a comprehensive overview of the current state-of-the-art methods and challenges in processing sEMG signals using deep learning techniques, with a focus on the taxonomy, applications, and open issues Did not provide a quantitative comparison or evaluation of different methods; did not address the ethical or social issues related to sEMGbased applications T. Zhou, Y. Wang, and J. Du [27] Feature grouping and deep learning to predict human hand motion trajectories from sEMG signals during pipe skid maintenance tasks with high accuracy and efficiency Proposed a novel method that uses feature grouping and deep learning to predict human hand motion trajectories from sEMG signals during pipe skid maintenance tasks with high accuracy and efficiency Used a small dataset of 10 subjects; did not test the method on other tasks or scenarios or compare it with other prediction methods M. F. Wahid and R. Tafreshi [41] Regularized common spatial pattern (RCSP) with majority voting strategy to improve the classification accuracy of motor imagery tasks from EEG signals for BCI applications Proposed a novel method that uses regularized common spatial pattern (RCSP) with majority voting strategy to improve the classification accuracy of motor imagery tasks from EEG signals for BCI applications Used a small dataset of 14 subjects; did not compare the performance with other methods or evaluate the usability of the BCI system A. K. Mukhopadhyay and S. Samui [42] Deep neural network to classify upper limb movements from sEMG signals regardless of the arm position with high accuracy and robustness Proposed a novel method that uses a deep neural network to classify upper limb movements from sEMG signals regardless of the arm position with high accuracy and robustness Used a small dataset of 10 subjects; did not test the method on different activities or compare it with other position invariant methods…”

Section: Figure 1 Clustering Deep Learning Application For Medical Re...mentioning

confidence: 99%

How Deep Learning and Neural Networks can Improve Prosthetics and Exoskeletons: A Review of State-of-the-Art Methods and Challenges

Triwiyanto,

Caesarendra,

Ahmed

et al. 2023

j.electron.electromedical.eng.med.inform

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Deep learning and neural networks are powerful computational methods that have been widely applied in various fields, such as healthcare and robotics. In this paper, we review some of the recent research studies that use deep learning and neural networks in healthcare and robotics, particularly focusing on their application in prosthetics and exoskeletons. The main source of data for this review is Scopus, which is a large and multidisciplinary database of peer-reviewed literature. The search criteria for this review are exoskeleton AND prosthetic AND deep AND learning. The search is limited to documents published from 2014 to 2023, as this period covers the recent developments and trends in the field4. The search results in 488 documents that match the criteria. We selected 20 papers that represent the state-of-the-art methods and applications of deep learning and neural networks in prosthetics and exoskeletons. We categorized these papers by various attributes, such as document type, subject area, sensor type, respondent, condition, etc. The main finding of this paper was that deep learning techniques and neural networks have diverse and transformative potential in healthcare and robotics, especially in the development and improvement of prosthetics and exoskeletons. The paper highlighted how these advanced computational methods can be harnessed to interpret complex biological signals, improve device functionality, enhance user safety, and ultimately improve quality of life for individuals using these devices. The paper also identified some possible future directions for this topic, such as exploring the impact of deep learning techniques and neural networks on the performance, usability, and user satisfaction of prosthetics and exoskeletons. This paper provided a valuable insight into the current state-of-the-art and future prospects of deep learning techniques and neural networks in healthcare and robotics

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“…The model achieved an accuracy of 86.64%, a loss of 0.46, and an average F1 score of 0.6318 across all five different stages of DR. Furthermore, other researchers [44,45] reviewed the contributions of numerous studies in the field of DR detection and classification, highlighting the implementation of both ML and DL models in these endeavors. In recent years, several research studies have explored innovative techniques and ap-plications in various fields.…”

Section: Related Studiesmentioning

confidence: 99%

The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning

2023

IJFMR

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Diabetic retinopathy is a severe problem of long-term diabetes, which affects the human eye and gradually leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classi- fier, and the DenseNet 121 network, for Diabetic retionopathy detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits not balanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The better performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can sig- nificantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

show abstract

Diabetic Retinopathy Detection Using Convolutional Neural Network—A Study

Cited by 7 publications

References 7 publications

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

How Deep Learning and Neural Networks can Improve Prosthetics and Exoskeletons: A Review of State-of-the-Art Methods and Challenges

The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning

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