Classification Of Eye Diseases Research Articles

Clinical Decision Support System for Ophthalmologists for Eye Disease Classification

ABSTRACT In medical applications, ocular OCT (optical coherence tomography) is used to assess glaucoma, macular degeneration, diabetic macular edema and other eye diseases as it is capable of showing the cross-sections of tissue layers. The creation of new blood vessels in the choroid layer of the eye is known as choroidal neovascularization (CNV). The aging and macular degeneration will represent the symptom DRUSEN. Our sharp central vision is affected due to DRUSEN. An irreversible vision loss is caused in diabetic patients due to diabetic macular edema (DME). It is mainly due to the leaking of blood vessels in the retina. This research work focus on designing a clinical decision support system to assist the ophthalmologist in classifying the three different types of eye diseases. The existing nine pre-trained CNN models are used for this purpose. The extracted features are used to generate the trained model that is further used for eye disease classification. The training accuracy, validation accuracy, training loss and validation loss are computed for 100 iterations for each pre-trained CNN models during training and validation. The trained model obtained after training is used as input to the classifier, which classifies the images under-diagnosis into NORMAL (normal eye), CNV, DME and DRUSEN. The performance metrics of the classifier designed using each pre-trained models are evaluated and compared for four classes independently. The test results show that the performance of the classifier implemented using the pre-trained model InceptionV3 is better than all other models.

Optimized convolutional neural network-based comprehensive early diagnosis method for multiple eye disease recognition

The multilabel classification method (MCM) is an active and effective research area in image analysis for the detection of various diseases. MCM provides insights and assistance to ophthalmologists in the detection of eye disease at an early stage. We propose comprehensive age-related eye disease detection at an early stage using retinal fundus images that were taken from the online public dataset. The flower pollination optimization algorithm was used for the optimization of the hyperparameters of the deep convolutional neural network (DCNN), which increased the speed and accuracy of the network. Initially, training was performed using the public datasets. The overall improvement in training accuracy (7.5%) was achieved using the optimized method compared with the nonoptimized method. Then, the output of the DCNN was applied to a multiclass support vector machine for the classification of eye diseases. The performance of the proposed method was compared with that of the other optimization techniques with the help of the standard performance measures, namely accuracy, specificity, sensitivity, precision, and F1-score. Upon comparing the performance of the proposed method with that of the nonoptimized DCNN in terms of performance measures, it had an improvement in the detection accuracy of 9.62% for AMD, 3.57% for glaucoma, 6.59% for normal, 7.71% for DR, and 11.75% for cataract. The obtained results show the effectiveness of the proposed method.

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

Geometric and Volumetric Measurements of Orbital Structures in CT Scans in Thyroid Eye Disease Classification

This study examines the usefulness of both geometric and volumetric measurements of orbital soft tissues on CT scans to provide quantitative diagnostic guidance in image reading of thyroid eye disease (TED). Computed tomography (CT) images were obtained from 92 orbits and were classified as impaired motility (TED-IM) and normal motility (TED-NM). The TED-IM group was further divided into dysthyroid optic neuropathy (DON) and non-DON groups. There were 5 volumetric, 2 angular, and 3 ratio parameter measurements acquired from CT images to examine their feasibility in TED classification. We found that the mean volumes of extraocular muscle and retroorbital fat and their ratio to the orbital volume were significantly different between the two motility groups. The mean ratio of extraocular muscle volume in orbital apex and orbital apex volume (EMV-OA/OAV) was significantly larger in DON than non-DON patients (p < 0.05). The population distribution among TED-NM, non-DON, and DON groups significantly varied for different angles between the optic nerve and medial rectus and lateral rectus. In conclusion, geometric and volumetric measurements using CT scans help to quantitatively classify TED.

Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D Filter.

Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods.

Eye Diseases Classification Using Back Propagation With Parabola Learning Rate

Human eye disease classification adapted by many pieces of research in the last decade due to the importance of eye organ for humans and the evolution in classification techniques. Seven external eye diseases plus normal eye classified in this paper using backpropagation with linear cyclic learning rate-based parabola function. The classification accuracy achieved in this paper is (89.83%).

Eye Diseases Classification Using Back Propagation with Welch Estimation Based-Learning Rate

Human eye disease classification adapted by many pieces of research in the last decade due to the importance of eye organ for humans and the evolution in classification techniques. External eye diseases classified in this paper using backpropagation with non-linear cyclic learning rate based on welch estimation as a step for improving the performance of backpropagation. As a result, Classification accuracy achieved in this paper is (93.22%).

Automatic Detection of Genetics and Genomics of Eye Disease Using Deep Assimilation Learning Algorithm.

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.

Classification of Eye Diseases in Fundus Images

Eye diseases have been a severe problem worldwide, especially in developing countries where technology and finance are limited. Today, the problem is being resolved thanks to the task of classification that is part of pattern recognition. Its primary goal is to group standard features from any entity, object, phenomenon, or event belonging to the real or abstract world. Convolutional Neural Networks are a type of Artificial Neural Network used in intelligent pattern classification, Machine Learning, and Data Mining. Also, medicine and ophthalmology used these algorithms for detecting diseases in the human body. This work presents a novel intelligent pattern classification algorithm based on a Convolutional Neural network, which is validated through the K-Fold Cross Validation test. Two different groups of retinography images are given: Glaucoma and Diabetic Retinopathy. The result of accuracy percentage was 99.89%. Numerical metrics: Accuracy, Recall, Specificity Precision, and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score with values close to 1, and ROC curves support the suitable performance of the proposed classifier.

Deep convolutional neural network-based diabetic eye disease detection and classification using thermal images

Infrared thermography which is non-contact and non-invasive technique is widely accepted as a medical diagnostic tool. Thermal images are processed for abnormality detection and quantification. It has been used in the diagnosis of dry eye, meibomian gland dysfunction, thyroid eye disease and glaucoma. Diabetic eye disease (DED) detection using thermal images is an absolutely new attempt. The early detection of the occurrence of DED can be very helpful for clinical treatment. In this paper, we are attempting towards finding an automatic way to classify DEDs in thermal images using a deep learning-based convolutional neural network (CNN) methodology. The sensitivity of 92.30%, specificity of 98.46%, and accuracy of 95.38% on testing dataset with reference to expert's ground truth results are obtained. The results attained evidently exhibit that the thermal imaging is promising modality and proposed deep learning method is capable for automatic diagnosis of diabetic eye disease classification.

Quantitative optical coherence tomography angiography: A review.

As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed.Impact statementOCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. Quantitative analysis of OCTA is essential to standardize objective interpretations of clinical outcome. This review summarizes technical rationales and clinical applications of quantitative OCTA features.

Eye Disease Classification based on Deep Belief Networks

Problems affecting the eye can range from infections of the eye or the eyelid, genetically inherited eye problems, eye injuries or objects in the eye, and eye disorders that are the result of conditions that affect many organs. While classification of disease stages is critical to understanding disease risk and progression, several systems based on red tissues eye photographs are known. Most of these require in-depth and time-consuming analysis of eye images. Herein, we present an automated computer-based classification algorithm using Deep belief networks (DBNs), as well as network initialization effect to classify isolated events. Classification is a data mining (machine learning) technique used to predict group membership for data instances. In Image classification analyzes the numerical properties of various image features and organizes data into categories. To simplify the problems of prediction or classification, neural networks are being introduced. In this paper, the introduction of Deep- belief network (DBN) to classify the types of diseases affects the human eye. The DBN classify the three types of diseases such as defected eye, conjunctivitis, Keratoconus, and other types of diseases. The eye diseases features are compared with the features of the database. Finally the classification output produces the types of diseases present in the eye image. The implementations are done using the Matlabsoftware. Finally, the classification performances of DBN architecture were analyzed using the accuracy, precision and recall parameters etc.

Data Driven Approach for Eye Disease Classification with Machine Learning

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

Integrating Handcrafted and Deep Features for Optical Coherence Tomography Based Retinal Disease Classification

Deep neural networks (DNNs) have been widely applied to the automatic analysis of medical images for disease diagnosis and to help human experts by efficiently processing immense amounts of images. While the handcrafted feature has been used for eye disease detection or classification since the 1990s, DNN was recently adopted in this area and showed a very promising performance. Since handcrafted and deep feature can extract complementary information, we propose, in this paper, three different integration frameworks to combine handcrafted and deep feature for optical coherence tomography image-based eye disease classification. In addition, to integrate the handcrafted feature at the input and fully connected layers using existing networks, such as VGG, DenseNet, and Xception, a novel ribcage network (RC Net) is also proposed for feature integration at middle layers. For RC Net, two “rib” channels are designed to independently process deep and handcrafted features, and another so-called “spine” channel is designed for the integration. While dense blocks are the main components of the three channels, sum operation is proposed for the feature map integration. Our experimental results showed that the deep networks achieved better classification accuracy after the integration of the handcrafted features, e.g., scale-invariant feature transform and Gabor. The RC Net showed the best performance among all the proposed feature integration methods.

Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible?

Diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of vision loss and blindness worldwide. They tend to be asymptomatic in the early phase of disease and therefore require active screening programs to identify the patients requiring referral and treatment. Deep learning-based artificial intelligence technology has recently become a major topic in the field of ophthalmology. This paper aimed to provide a general view of the major findings on the application of deep learning for the classification of eye diseases from common imaging modalities. In the future, it is expected that these technologies will be applied in real-world screening programs to improve their efficiency and affordability.

Classification of eye diseases using optic cup segmentation and optic disc ratio and its implementation using VHDL

Classification of eye diseases using optic cup segmentation and optic disc ratio and its implementation using VHDL

Classification of eye diseases using optic cup segmentation and optic disc ratio and its implementation using VHDL

The proposed work consists reprocessing using morphological operations and then segmentation of optic disc and optic cup by using the morphological operations. These operations are minimizing errors detection limit of the optic disc due to blood vessels cross. Then, the cup to disc ratio (CDR) is calculated using these operations. The cup to disc ratio is more than 0.3 then patients are glaucoma otherwise normal. Spatially Weighted Fuzzy C Mean (SWFCM) clustering method is used to segment the optic disc and Superpixel algorithm is used to segment the optic cup. The segmented optic disc and cup are then used to compute the CDR for glaucoma screening. Optic disc and cup segmentation of fundus image are considered for Diabetic Retinopathy Detection and Glaucoma detection. In this paper, we are considering one simple image and its part will be implemented by using VHDL family Altra Quartus II, which is a programmable logic device design software produced by Altera. Quartus II enables analysis and synthesis of HDL designs, which enables the developer to compile their designs, perform timing analysis, examine RTL diagrams, simulate a design's reaction to different stimuli, and configure the target device with the programmer. Quartus includes an implementation of VHDL and Verilog for hardware description, visual editing of logic circuits, and vector waveform simulation. The implemented results using MATLAB shows accurate results obtained both for diabetic retinopathy as well as glaucoma.

Assessment of sparse-based inpainting for retinal vessel removal

Some important eye diseases, like macular degeneration or diabetic retinopathy, can induce changes visible on the retina, for example as lesions. Segmentation of lesions or extraction of textural features from the fundus images are possible steps towards automatic detection of such diseases which could facilitate screening as well as provide support for clinicians. For the task of detecting significant features, retinal blood vessels are considered as being interference on the retinal images. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The performance of the algorithm is tested for greyscale and RGB images from the DRIVE and STARE public databases, employing different neighbourhoods and sparseness factors. Moreover, a comparison with the most common inpainting family, diffusion-based methods, is carried out. For this purpose, two different ways of assessing the quality of the inpainting are presented and used to evaluate the results of the non-artificial inpainting, i.e. where a reference image does not exist. The results suggest that the use of sparse-based inpainting performs very well for retinal blood vessels removal which will be useful for the future detection and classification of eye diseases.

Classification of Eye Diseases Using Optic Cup Segmentation and Optic Disc Ratio

The retinal images are widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy, cataract and glaucoma. Normally retinal images are classified manually by clinicians trained in time and process resources. Segmentation imaging of the eye retina provides an important role in the calculation the shape and size of the optic disc and the anterior segment and the abnormal growth of any geometry in the eye region. It automatically and precisely calculates the values of location, position and contour area and a structural part of the image required by ophthalmologists. When there is an elevated intra ocular pressure from the normal condition, the subject is affected by glaucoma and in this condition the retinal nerve fiber layer and the optic disc are affected and this leads to progressive loss of vision. This paper proposes a novel method for glaucoma screening by using optic disc & cup segmentation. But , the segmentation methods suffer from many problems such as the optimization, and initialization inadequate in noisy images results. In this paper, we are tried to locate the most important region of the eye optic disc and optic cup using a morphological operations. The optic disc is the area on the retina optic nerve axons that enter and leave the eye. The proposed work consists pre processing using morphological operations and then segmentation of optic disc and optic cup by using the morphological operations. These operations are minimizing errors detection limit of the optic disc due to blood vessels cross. Then, the cup to disc ratio (CDR) is calculated using these operations. The cup to disc ratio is more than 0.3 then patients are glaucoma otherwise normal. Spatially Weighted Fuzzy C Mean (SWFCM) clustering method is used to segment the optic disc and Superpixel algorithm is used to segment the optic cup. The segmented optic disc and cup are then used to compute the CDR for glaucoma screening. Optic disc and cup segmentation of fundus image is considered for Diabetic Retinopathy Detection and Glucoma detection. The implemented results using MATLAB shows accurate results obtained both for diabetic retinopathy as well as glaucoma.