A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning

Ramchandre, Satwik; Patil, Bhargav; Pharande, Shardul; Javali, Karan; Pande, Himangi

doi:10.1109/inocon50539.2020.9298201

Cited by 25 publications

(3 citation statements)

References 7 publications

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“…The model is trained using a step-size of 0.002 with total of 75 cycles using transfer learning. Two models, SEResNeXt32x4d and EfficientNetb3 were used [5].…”

Section: Literature Surveymentioning

confidence: 99%

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

2023

ijfans

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Diabetic retinopathy (DR), which causes tissue on the eye that damages visibility, is a common complication of type-2 diabetes. If it is not discovered in time, total blindness might occur. DR is irreversible. DR is primarily among adults who are of working age. More than 150 million people are affected by diabetic retinopathy (DR), which accounts for 2.6% of blindness worldwide. Different indications of DR are vision distortion, bulging of the eye, and formation of irregular blood vessels. The traditional way is to use Computer-aided Diagnosis (CAD) systems during treatment. The dataset used is the APTOS blindness detection dataset that is accessible in Kaggle. The Convolutional Neural Networks (CNN) is the most effective way for classifying images. In this paper, the MobileNet architecture, a deep learning technique is utilized to automate the diagnosis of the disease and estimate the severity of the eye into several stages through which the accuracy obtained for training is 95% and validation is 82%.

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“…The model is trained using a step-size of 0.002 with total of 75 cycles using transfer learning. Two models, SEResNeXt32x4d and EfficientNetb3 were used [5].…”

Section: Literature Surveymentioning

confidence: 99%

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

2023

ijfans

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“…A breakthrough in the field of quantum computing can help in giving the ophthalmologist a second opinion to solve this problem by using hybrid quantum transfer learning approach. This quantum approach can result into more efficient detection of DR in patients as compared to the classical transfer learning [3,4]. Quantum transfer learning and Principal Component Analysis (PCA) is currently used in various medical diagnostics [2].…”

Section: Figurementioning

confidence: 99%

Diabetic Retinopathy Detection Using Classical-Quantum Transfer Learning Approach and Probability Model

Mir¹,

Yasin²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

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Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically. Thus, our proposed methodology aims to detect DR using a hybrid quantum transfer learning approach. To build our model we extract the APTOS 2019 Blindness Detection dataset from Kaggle and used inception-V3 pre-trained classical neural network for feature extraction and Variational Quantum classifier for stratification and trained our model on Penny Lane default device, IBM Qiskit BasicAer device and Google Cirq Simulator device. Both models are built based on PyTorch machine learning library. We bring about a contrast performance rate between classical and quantum models. Our proposed model achieves an accuracy of 93%-96% on the quantum hybrid model and 85% accuracy rate on the classical model. So, quantum computing can harness quantum machine learning to do work with power and efficiency that is not possible for classical computers.

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“…The classification problems related to DR can be broadly categorized into two types: binary and multiclass. Binary classification focuses on distinguishing between a diseased retina and a healthy retina in color fundus images, as supported by the research [ 26 , 27 ]. On the other hand, multiclass classification approaches aim to grade the images into five different categories: Class 0—non DR, Class 1—mild DR, Class 2—moderate DR, Class 3—severe DR, and Class 4—proliferative DR [ 28 , 29 ].…”

Section: Introductionmentioning

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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A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning

Cited by 25 publications

References 7 publications

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

Diabetic Retinopathy Detection Using Classical-Quantum Transfer Learning Approach and Probability Model

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

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