A deep transfer learning approach for identification of diabetic retinopathy using data augmentation (original) (raw)
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Diabetic Retinopathy Classification using Transfer learning
International Journal of Advanced Trends in Computer Science and Engineering , 2023
Diabetic Retinopathy (DR) is an eye illness that impacts individuals who have diabetes and damages their retina over time, eventually causing blindness. Due to lesions in the retina that are formed because of retinal blood vessel rupture, it impairs vision and, in the worst-case scenario, results in severe blindness. To prevent severity and to lessen challenges in identifying tiny lesions throughout the disease's advanced stages, it is now crucial to diagnose the condition early as, it manifests itself without any symptoms. Even ophthalmologists find it challenging and time-consuming to identify this condition. Early DR case identification and classification is essential for delivering the required medical care. This study proposes applying deep learning techniques to detect DR in retinal fundus images. The data acquired for this process may be incomplete and imbalanced. Data augmentation balances the data and increase the quantity of retinal images. As deep-learning algorithms need more data to process, DCGAN Augmentation technique is employed. The CNN (Convolutional Neural Network) methods, specifically the VGG16 and DenseNet121 architectures, are employed for DR early detection in order to let patients to receive therapy at the appropriate time..
AECE, 2021
Diabetic Retinopathy (DR) stays a main source of vision deterioration around world and it is getting exacerbated day by day. Almost no warning signs for detecting DR which will be greater challenge with us today. So, it is extremely preferred that DR has to be discovered on time. Adversely, the existing result involves an ophthalmologist to manually check and identify DR by positioning the exudates related with vascular irregularity due to diabetes from fundus image. In this work, we are able to classify images based on different severity levels through an automatic DR classification system. To extract specific features of image without any loss in spatial information, a Convolutional Neural Network (CNN) models which possesses an image with a distinct weight matrix is used. In the beginning, we estimate various CNN models to conclude the best performing CNN for DR classification with an objective to obtain much better accuracy. In the classification of DR disease with transfer learning using deep CNN models, 97.72% of accuracy is provided by the proposed CNN model for Kaggle dataset. The proposed CNN model provides a classification accuracy of 97.58% for MESSIDOR dataset. The proposed technique provides better results than other state-ofart methods.
A Comparative Study of Deep Learning and Transfer Learning in Detection of Diabetic Retinopathy
International Journal of Computer Applications Technology and Research
Computer vision has gained momentum in medical imaging tasks. Deep learning and Transfer learning are some of the approaches used in computer vision. The aim of this research was to do a comparative study of deep learning and transfer learning in the detection of diabetic retinopathy. To achieve this objective, experiments were conducted that involved training four state-of-the-art neural network architectures namely; EfficientNetB0, DenseNet169, VGG16, and ResNet50. Deep learning involved training the architectures from scratch. Transfer learning involved using the architectures which are pre-trained using the ImageNet dataset and then fine-tuning them to solve the task at hand. The results show that transfer learning outperforms learning from scratch in all three models. VGG16 achieved the highest accuracy of 84.12% in transfer learning. Another notable finding is that transfer learning is able to not only achieve high accuracy with very few epochs but also starts higher than deep...
A transfer learning with deep neural network approach for diabetic retinopathy classification
International Journal of Electrical and Computer Engineering (IJECE), 2021
Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural network (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY
DergiPark (Istanbul University), 2022
Diabetes is a highly prevalent and increasingly common health disorder, resulting in health complications such as vision loss. Diabetic retinopathy (DR) is the most common form of diabetescaused eye disease. Early diagnosis and treatment are crucial to prevent vision loss. DR is a progressive disease composed of five stages. The accurate diagnosis of DR stages is highly important in guiding the treatment process. In this study, we propose a deep transfer learning framework for automatic detection of DR stages. We examine our proposed model by comparing different convolutional neural networks architectures: VGGNet19, DenseNet201, and ResNet152. Our results demonstrate better accuracy after applying transfer learning and hyper-parameter tuning to classify the fundus images. When the general test accuracy and the performance evaluations are compared, the DenseNet201 model is observed with the highest test accuracy of 82.7%. Among the classification algorithms, the highest AUC value is 94.1% obtained with RestNet152.
Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images
Brain Sciences
Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method. Although deep learning models provide automatic feature extraction and classification, training such models from scratch requires a larger annotated dataset. The availability of annotated training datasets is considered a core issue for implementing deep learning in the classification of medical images. The models based on transfer learning are widely adopted by the researchers to overcome annotated data insufficiency problems and computational overhead. In the proposed study, features are extracted from fundus images using the pre-trained ...
1. We developed a novel predictive model based on Efficient Net b3 pre-trained Convolutional Neural Network for reliable classification of Diabetic Retinopathy into 5 Stages, namely: 0 – No DR, 1 – Mild DR, 2 – Moderate DR, 3 – Severe DR, 4 – Proliferate DR. These 5 stages are based on the severity of diabetic retinopathy. Since treatment varies based on the severity of diabetic retinopathy. So, if 5 stages are predicted appropriately then it helps doctor to diagnose the patient based on the stage. 2. We used three different pre-trained CNN models: VGG Net, Res Net (which are scaled by their depth to get higher versions), and Efficient Net (which uses an efficient compound scaling method that scales width, height, and resolution to get higher versions). We observed Efficient Net could classify the images into more than one class, unlike Res Net and VGG Net. The reason could be models like Res Net and VGG Net is not capable of extracting complex features from all the stages of diabet...
Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
2019
Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems. Transfer learning from an already trained deep convolutional network can be used to reduce the cost of training from scratch and to train with small training data for deep learning. This raises the question of whether we can use transfer learning to overcome the training data insufficiency problem in deep learning based medical data classifications. Deep convolutional networks have been achieving high performance results on the ImageNet Large Scale Visual Recognition Competition (ILSVRC) image classification challenge. One example is the Inception-V3 model that was the first runner up on the ILSVRC 2015 challenge. Inception modules that help to extract different sized features of input images in one level of convolution are the unique features of the Inception-V3. In this work, we have used a pre-trained Inception-V3 model to take advantage of its Inception modules for Diabetic Retinopathy detection. In order to tackle the labelled data insufficiency problem, we subsampled a smaller version of the Kaggle Diabetic Retinopathy classification challenge dataset for model training, and tested the model's accuracy on a previously unseen data subset. Our technique could be used in other deep learning based medical image classification problems facing the challenge of labeled training data insufficiency.
Predictive Analysis of Diabetic Retinopathy with Transfer Learning
2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), 2021
With the prevalence of diabetes, Diabetes Mellitus Retinopathy (DR) is becoming a major health problem across the world. The long-term medical complications arising due to DR significantly affect the patient as well as the society, as the disease mostly affects young and productive individuals. Early detection and treatment can help reduce the extent of damage to the patients. The rise of Convolutional Neural Networks for predictive analysis in the medical field paves the way for a robust solution to DR detection. This paper studies the performance of several highly efficient and scalable CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The research focuses on VGG16, Resnet50 V2, and EfficientNet B0 models. The classification performance is analyzed using several performance measures including True Positive Rate, False Positive Rate, Accuracy, etc. Also, several performance graphs are plotted for visualizing the architecture performance i...
Reducing Overfitting in Diabetic Retinopathy Detection using Transfer Learning
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020
Diabetic retinopathy is a diabetes complication which causes damage to the retina. It is highly prevalent in patients who have had diabetes for 20 years or more. Current deep learning methods of detection of this disease are not efficient with the respect to the number of images required for training and the transfer learning methods use pre-trained networks, which contains many features that are irrelevant to the original task data. In this paper, we pre-train our network in a self supervised manner on the training data, such that the network learns features relevant to the task data without overfitting. We propose a data efficient architecture based on autoencoder and VGG network and a method to train the network such that it is regularized by an autoencoder network to prevent overfitting and improve test data performance.