Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy (original) (raw)
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Diabetic retinopathy grading system based on transfer learning
International Journal of Advanced Computer Research, 2021
Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) automatically and accurately. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose the various DR grades. Recently, many CAD systems based on deep learning (DL) methods have been adopted to get deep learning merits in diagnosing the pathological abnormalities of DR disease. In this paper, we present a full based-DL CAD system, depending on multilabel classification. In the proposed DL CAD system, we present a customized EffecientNet model in order to diagnose the early and advanced grades of the DR disease based on transfer learning. Transfer learning is very useful in training small datasets. We utilized a multi-label Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset. The experiments manifest that the proposed DL CAD system is robust, reliable, and deigns promising results in detecting and grading DR. The proposed system achieved accuracy (ACC) equals 86%, and the Dice similarity coefficient (DSC) equals 78.45%.
Diabetic Retinopathy Detection and Grading using Deep learning
MJEER, 2022
One of the complications of diabetes disease is diabetic retinopathy (DR). Diabetic patients may suffer from total loss of sight. That's if it is not detected and medicated early enough. The early detection of DR is very important during funds screening on a regular basis. Detection and grading of DR are difficult because most fundus images suffer from undersaturation and noise. This paper proposes a new enhancement process as a solution to the poor quality of fundus images. It also proposes two architectures for convolutional neural network (CNN) models. The first one is the binary classifier of DR images into normal and abnormal. The second CNN architecture to classify the severity grades of DR. In this study, we also utilized different pre-trained convolutional neural network models to show the impact on the performance of the use of transfer learning from pre-trained CNN models vs newly defined architectures. The pre-trained CNN models and the two new proposed CNN models are tested using Messidor1, Messidor2, and Kaggle EyePACS datasets. The proposed binary classifier model results in F1-scores of 0.9387, 0.9629, and 0.9430 on the Messidor-1, Messidor-2, and EyePACS datasets, respectively. The proposed second model classifies the five grades with an F1-score of 0.9133, 0.9226, and 0.9393 on the Messidor1, Messidor2, and Kaggle EyePACS datasets, respectively. The new proposed CNN model proved its reliability and efficiency in detecting DR and classifying severity grades of DR in fundus images. Preprocessing techniques enhanced the performance by 10.83% of accuracy and 0.13037 in AUC using the binary model.
Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions
IEEE Access
Multi-label classification (MLC) is considered an essential research subject in the computer vision field, principally in medical image analysis. For this merit, we derive benefits from MLC to diagnose multiple grades of diabetic retinopathy (DR) from various colored fundus images, especially from multi-label (ML) datasets. Therefore, ophthalmologists can detect early signs of DR as well as various grades to initiate appropriate treatment and avoid DR complications. In this paper, we propose a comprehensive ML computer-aided diagnosis (CAD) system based on deep learning technique. The proposed system's main contribution is to detect and analyze various pathological changes accompanying DR development in the retina without injecting the patient with dye or making expensive scans. The proposed ML-CAD system visualizes the different pathological changes and diagnoses the DR grades for the ophthalmologists. First, we eliminate noise, enhance quality, and standardize the sizes of the retinal images. Second, we differentiated between the healthy and DR cases by calculating the gray level run length matrix average in four different directions. The system automatically extracts the four changes: exudates, microaneurysms, hemorrhages, and blood vessels by utilizing a deep learning technique (U-Net). Next, we extract six features, which are the gray level co-occurrence matrix, areas of the four segmenting pathology variations, and the bifurcation points count of the blood vessels. Finally, the resulting features were afforded to an ML support vector machine (SVM) based on a classifier chain to differentiate the various DR grades. We utilized eight benchmark datasets (four of them are considered ML) and six different performance evaluation metrics to evaluate the proposed system's performance. It achieved 95.1%, 91.9%, 86.1%, 86.8%, 84.7%, 86.2% for accuracy, area under the curve, sensitivity, specificity, positive predictive value, and dice similarity coefficient, respectively. The experiments show encouraging results as compared with other systems. INDEX TERMS Multi-label computer-aided diagnosis (ML-CAD), multi-label classification (MLC), deep learning (DL), U-Net, diabetic retinopathy (DR).
Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC
Intelligent Automation & Soft Computing
The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.
Automated Detection of Diabetic Retinopathy Using Digital Fundus Images
Significant amount of people suffer from Diabetic Retinopathy (DR), which is one of the major causes of vision loss. The incidence of this disease is even higher due to not being diagnosed at the right time. On numerous occasions, due to neglect and poor care, diabetic retinopathy can lead to significant damage to the eyes. That is why, early diagnosis of eye diseases, proper treatment and care for the disease can prevent vision loss. Referral of eyes with diabetic retinopathy for advanced assessment and treatment would aid in reducing the chances of vision loss, allowing proper diagnoses. The purpose of this study is to develop resilient and flexible diagnostic techniques for the detection of DR and to identify dynamic DR grading using residual networks to facilitate the network training that are significantly intense than previously used networks. Even though lots of research has been done on DR, its identifications remains challenging due to time and space complexity along with higher accuracy specificity. Here, a residual learning framework has been proposed that overcomes the challenges while efficiently detecting DR. Hence, using a high-end Graphics Processor Unit (GPU) the model has been trained on the publicly available Kaggle dataset and empirical evidence has been provided in order to support the results with a sensitivity of 95.6% and an accuracy of 93.20%.
Elsevier, 2023
Diabetic Retinopathy (DR) is the most common cause of eyesight loss that affects millions of people worldwide. Although there are recognized screening procedures for detecting the condition, such as fluorescein angiography and optical coherence tomography, the majority of patients are unaware and fail to have such tests at the proper time. Prompt identification of the condition is critical in avoiding vision loss, which occurs when Diabetes Mellitus (DM) is left untreated for an extended length of time. Several Machine Learning (ML) and Deep Learning (DL) algorithms have been used on DR datasets for disease prediction and classification, however, the majority of them have ignored the element of data pre-processing and dimensionality reduction, which are known as a major gap resulting in biased findings. In the first line of this research, data preprocessing was performed on the color Fundus Photographs (CFPs). Subsequently, we performed feature extraction with Principal Component Analysis (PCA). A Deep Learning Multi-Label Feature Extraction and Classification (ML-FEC) model based on pre-trained Convolutional Neural Network (CNN) architecture was proposed. Then, transfer learning was applied to train a subset of the images using three state-of-the-art CNN architectures, namely, ResNet50, ResNet152, and SqueezeNet1 with parameter-tuning to identify and classify the lesions. The experimental findings revealed an accuracy of 93.67% with a hamming loss of 0.0603 for ResNet 50, an accuracy of 91.94%, and Hamming Loss of 0.0805 for Squeezenet1 and an accuracy of 94.40% with Hamming loss of 0.0560 was achieved by ResNet 152 which demonstrates the suitability of the model for implementation in daily clinical practice and to support large scale DR screening programs.
International Journal of Health Sciences (IJHS), 2022
Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. Many researchers have supported the ophthalmologists to diagnosis and classify the stage of the disease in the retinal fundus images using machine learning model. Machine learning models are less impressive for several staging diseases due to clinical grading. In order to develop a system capable of classifying the lesion grading images on disease pathology, a unique deep learning architecture named as discriminative Convolution Neural Network has been employed towards diagnosis of diabetic retinopathy through classification and progression prediction of lesion grading in fundus image of Retina. Initially Image Pre-processing has been carried out using wiener filter to remove the noise and Contrast limited adaptive histogram equalization for image enhancement. Pre-processed image has been processed using Oriented Fast and Rotated Brief for feature descriptors. It contains like Optic distance, Fovea, blood Vessel, Blot haemorrhages, Exudate number, exudates area, Macular Edema, Bifurcation, Shannon entrophy, Kapur Entropy and Renyis Entropy, LBP entropy, LBP energy and Microaneurysm. On these obtained features, feature reduction has to be carried out using principle component analysis to eliminate the irrelevant features before the onset of the process. Reduced features have been segmented into lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates) and disease grading for Diabetic Retinopathy and Diabetic Macular Edema Severity Grade on dividing the images into training and testing Finally proposed model achieves better results and outperforms other state of art approaches in identifying lesion stages on subtle features with mean sensitivity of 98.89% and mean predictive value of 97.56%.
Evaluation of a Computer-Aided Diagnosis System for Diabetic Retinopathy Screening on Public Data
Investigative Ophthalmology & Visual Science, 2011
PURPOSE. To evaluate the performance of a comprehensive computer-aided diagnosis (CAD) system for diabetic retinopathy (DR) screening, using a publicly available database of retinal images, and to compare its performance with that of human experts. METHODS. A previously developed, comprehensive DR CAD system was applied to 1200 digital color fundus photographs (nonmydriatic camera, single field) of 1200 eyes in the publicly available Messidor dataset (Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology (http://messidor.crihan.fr). The ability of the system to distinguish normal images from those with DR was determined by using receiver operator characteristic (ROC) analysis. Two experts also determined the presence of DR in each of the images. RESULTS. The system achieved an area under the ROC curve of 0.876 for successfully distinguishing normal images from those with DR with a sensitivity of 92.2% at a specificity of 50%. These compare favorably with the two experts, who achieved sensitivities of 94.5% and 91.2% at a specificity of 50%. CONCLUSIONS. This study shows, for the first time, the performance of a comprehensive DR screening system on an independent, publicly available dataset. The performance of the system on this dataset is comparable with that of human experts.
Computer Aided Diagnosis of Eye Disease for Diabetic Retinopathy
Journal of Physics: Conference Series, 2019
Diabetic retinopathy (DR) is one of diabetes complication that could cause the vision loss, where it is caused by the damage of the blood vessels at the back of the eye. Due to this, regular eye checkup and timely treatment is needed. However, the lack of specialized ophthalmologists together with associated higher medical costs makes regular checkup costly. Therefore, any application of the technologies such as Computer Aided Diagnosis (CAD) system that could help in analysing DR efficiently in its early stage may help this current situation. Although CAD systems were developed before, but the graphic user interface for user is not developed for the ease of uses for everyone and not just limited to professional. So, in this study, a system is created in order to help the doctor to reduce their burden on the job daily and the false negative rate for the benefit of the patient. The input of the system is the Retinal fundus images (RFI) from STARE database, and the system was built with the ability to enhance and process the image for confirmation of DR. In addition, the system will help to extract out the important features based on Grey Level Co-Occurrence Matrix (GLCM) and classify it using artificial neural network (ANN) whether the patient is associated with the characteristics of DR. Also, the system will be easy to use to everyone as it will have its own graphic user interface to make it clear to everyone not just professionals so that the image from the RFI can be inserted and the result will come out in a short duration of time. The developed system able to achieve as high as 88% sensitivity, 80% specificity and 84% accuracy.
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images
Journal of Medical Systems, 2007
Purpose: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses. Design: We developed and evaluated a data-driven deep learning algorithm as a novel diagnostic tool for automated DR detection. The algorithm processed color fundus images and classified them as healthy (no retinopathy) or having DR, identifying relevant cases for medical referral. Methods: A total of 75 137 publicly available fundus images from diabetic patients were used to train and test an artificial intelligence model to differentiate healthy fundi from those with DR. A panel of retinal specialists determined the ground truth for our data set before experimentation. We also tested our model using the public MESSIDOR 2 and E-Ophtha databases for external validation. Information learned in our automated method was visualized readily through an automatically generated abnormality heatmap, highlighting subregions within each input fundus image for further clinical review. Main Outcome Measures: We used area under the receiver operating characteristic curve (AUC) as a metric to measure the precisionerecall trade-off of our algorithm, reporting associated sensitivity and specificity metrics on the receiver operating characteristic curve. Results: Our model achieved a 0.97 AUC with a 94% and 98% sensitivity and specificity, respectively, on 5-fold cross-validation using our local data set. Testing against the independent MESSIDOR 2 and E-Ophtha databases achieved a 0.94 and 0.95 AUC score, respectively. Conclusions: A fully data-driven artificial intelligenceebased grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment. The implementation of such an algorithm on a global basis could reduce drastically the rate of vision loss attributed to DR. Ophthalmology 2017;-:1e8 ยช 2017 by the American Academy of Ophthalmology Supplemental material is available at www.aaojournal.org.