Early Detection Of Eye Diseases Research Articles

A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.

In the context in which severe visual impairment significantly affects human life, this article emphasizes the potential of Artificial Intelligence (AI) and Visible Light Communications (VLC) in developing future assistive technologies. Toward this path, the article summarizes the features of some commercial assistance solutions, and debates the characteristics of VLC and AI, emphasizing their compatibility with blind individuals' needs. Additionally, this work highlights the AI potential in the efficient early detection of eye diseases. This article also reviews the existing work oriented toward VLC integration in blind persons' assistive applications, showing the existing progress and emphasizing the high potential associated with VLC use. In the end, this work provides a roadmap toward the development of an integrated AI-based VLC assistance solution for visually impaired people, pointing out the high potential and some of the steps to follow. As far as we know, this is the first comprehensive work which focuses on the integration of AI and VLC technologies in visually impaired persons' assistance domain.

Eye Disease Classification Based On Fundus Image Using Yolo V8 Algorithm

Eye disease is a very serious problem because it affects one of the five human senses. In many cases many people ignore the impact of eye diseases in the early stages. In general, the process of examining eye diseases is carried out based on manual analysis by doctors (experts) on the fundus image of the patient's eye at a fairly expensive cost. To overcome this, the author proposes an eye disease classification system that can automatically detect eye diseases using YOLO V8. This system can be used for early detection of eye diseases to prevent the development of more serious eye diseases. From the test results of the model built, the accuracy value is 92%, precision is 91%, recall is 92%, F1-score is 91%. Overall, these results can be considered satisfactory and can be implemented for eye disease classification systems based on fundus images.

Review of Eye Diseases Detection and Classification Using Deep Learning Techniques

Automated diagnosis of eye diseases using machine and deep learning models has become increasingly popular. Glaucoma, cataracts, diabetic retinopathy, Myopia, and age-related macular degeneration are common eye diseases that can cause severe damage. It is crucial to detect eye diseases early to prevent any potentially serious consequences. Early detection of eye disease is vital for effective treatment. Doing in-depth reading to identify any potential signs of eye disease is highly recommended. This paper will review all machine learning models built to detect and classify eye diseases in addition to helping grasp all limitations and challenges in this field. Recognizing eye diseases is a difficult task that typically requires several years of medical experience. This research is to be conducted to serve as a starting point for finding the most versatile solution. This research aims to review eye disease classification using deep learning models, including VGG16, ResNet, and Inception. The general classification model consists of these steps: The first step is to collect the globally obtainable datasets for the eye disease and pre-process them to ensure the generalization of experiments. The goal is to train the model to recognize disease symptoms instead of tweaking the outcomes for a specific dataset section. With the successful deployment of deep learning techniques for image classification and object recognition, research is now directed towards deep learning techniques instead of traditional handcrafted methods. One possible solution for the eye diseases classification challenge is to use a pre-trained deep CNN model for representation and feature extraction. This solution can be followed by classifier methods, such as support vector machines (SVM), multilayer perceptron (MLP), etc. It has been detected that CNN-based methods learned on large-scale marked datasets can be used for eye disease classification tasks with limited training datasets.

An Efficient Machine Learning Approach for Prediction of Conjunctiva Hyperemia Assessment using Feature Extraction Methods

The human eye is one of the most intricate sense organs. It is crucial to protect your eyes against several eye disorders that can cause vision loss if untreated in order to maintain your ability to see well. Early detection of eye diseases is therefore crucial in order to prevent any unintended consequences and control the diseases continued progression. Conjunctivitis is one such eye condition that is characterized by conjunctival inflammation, resulting in symptoms like hyperemia (redness) due to increased blood flow. With the aid of the best treatments, modern techniques, and early, precise diagnosis by professionals, it can be cured or can be greatly reduced. The proper diagnosis of the underlying cause of visual problems is frequently postponed or never carried out because of shortage of diagnostic experts, which leads to either insufficient or postponed corrective treatment. In order to diagnose and evaluate conjunctivitis, segmentation methods are essential for locating and measuring hyperemic regions. In the present study, segmentation techniques are applied along with feature extraction techniques to provide an effective machine learning framework for the prediction of eye problems. Using the discrete cosine transform (DCT), the segmented regions of interest are converted into feature vectors. These feature vectors are then used to train machine learning classifiers, including random forest and neural networks, which achieve a promising accuracy of 95.92%. This approach enables ophthalmologists to make more objective and accurate assessments, aiding in disease severity evaluation.

Artificial intelligence in ophthalmology.

One of the fields of medicine in which artificial intelligence techniques have made progress is ophthalmology. Artificial intelligence (A.I.) applications for preventing vision loss in eye illnesses have developed quickly. Artificial intelligence uses computer programs to execute various activities while mimicking human thought. Machine learning techniques are frequently utilized in the field of ophthalmology. Ophthalmology holds great promise for advancing artificial intelligence, thanks to various digital methods like optical coherence tomography (OCT) and visual field testing. Artificial intelligence has been used in ophthalmology to treat eye conditions impairing vision, including macular holes (M.H.), age-related macular degeneration (AMD), diabetic retinopathy, glaucoma, and cataracts. The more common occurrence of these diseases has led to artificial intelligence development. It is important to get annual screenings to detect eye diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration. These conditions can cause decreased visual acuity, and it is necessary to identify any changes or progression in the disease to receive appropriate treatment. Numerous studies have been conducted based on artificial intelligence using different algorithms to improve and simplify current medical practice and for early detection of eye diseases to prevent vision loss. Abbreviations: AI = artificial intelligence, AMD = age-related macular degeneration, ANN = artificial neural networks, AAO = American Academy of Ophthalmology, CNN = convolutional neural network, DL = deep learning, DVP = deep vascular plexus, FDA = Food and Drug Administration, GCL = ganglion cell layer, IDP = Iowa Detection Program, ML = Machine learning techniques, MH = macular holes, MTANN = massive training of the artificial neural network, NLP = natural language processing methods, OCT = optical coherence tomography, RBS = Radial Basis Function, RNFL = nerve fiber layer, ROP = Retinopathy of Prematurity, SAP = standard automated perimetry, SVP = Superficial vascular plexus, U.S. = United States, VEGF = vascular endothelial growth factor.

Remote Perimetry in a Virtual Reality Metaverse Environment for Out-of-Hospital Functional Eye Screening Compared Against the Gold Standard Humphrey Visual Fields Perimeter: Proof-of-Concept Pilot Study.

The growing global burden of visual impairment necessitates better population eye screening for early detection of eye diseases. However, accessibility to testing is often limited and centralized at in-hospital settings. Furthermore, many eye screening programs were disrupted by the COVID-19 pandemic, presenting an urgent need for out-of-hospital solutions. This study investigates the performance of a novel remote perimetry application designed in a virtual reality metaverse environment to enable functional testing in community-based and primary care settings. This was a prospective observational study investigating the performance of a novel remote perimetry solution in comparison with the gold standard Humphrey visual field (HVF) perimeter. Subjects received a comprehensive ophthalmologic assessment, HVF perimetry, and remote perimetry testing. The primary outcome measure was the agreement in the classification of overall perimetry result normality by the HVF (Swedish interactive threshold algorithm-fast) and testing with the novel algorithm. Secondary outcome measures included concordance of individual testing points and perimetry topographic maps. We recruited 10 subjects with an average age of 59.6 (range 28-81) years. Of these, 7 (70%) were male and 3 (30%) were female. The agreement in the classification of overall perimetry results was high (9/10, 90%). The pointwise concordance in the automated classification of individual test points was 83.3% (8.2%; range 75%-100%). In addition, there was good perimetry topographic concordance with the HVF in all subjects. Remote perimetry in a metaverse environment had good concordance with gold standard perimetry using the HVF and could potentially avail functional eye screening in out-of-hospital settings.

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features.

Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.

Early Detection of Eye Disease Using CNN

Abstract: The eyes perform an essential part of life. The eye is one of the organs that assist humans learn about their natural environments and collect information from them. Almost everywhere in the world, the frequency of eye disease has increased, needing a serious response. Immediate eye detection will be invaluable assistance in offering further treatment to prevent blindness. In this study, a Convolution Neural Network model is used for identifying eye diseases. This research aims to categorize human eyes into four categories: trachoma, conjunctivitis, cataract, and healthy. The investigation got an accuracy of 88.36%, while the CNN model evaluation provided Precision of 89.25%, Recall of 88.75%, and F1 Score of 88.5%. Based on the accuracy and evaluation results, this system can be used for the early detection of multiple eye diseases. Several random samples were also used for testing in this investigation. The test results indicate that this system is functional.

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography

BackgroundDifferential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity.MethodA deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR.ResultsIt was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups.ConclusionsAVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.

Ocular Complications in diabetic children in Aseer region

Background: Diabetes mellitus (DM), a multi-systemic disease marked by hyperglycaemia, is becoming more common around the world. Diabetes mellitus (DM) is a well-known cause of many ophthalmic problems, including diabetic retinopathy (DR), macular oedema, cataract, refractive change, and micro-vascular paralytic strabismus. A significant public health effort has been made to create ocular screening regimens for diabetic patients, beginning at a young age. Aim: to assess the knowledge of diabetic children regarding the risk of ocular complications and prevalence of ocular complications among diabetic children. Methodology: A descriptive cross-sectional study was used targeting all accessible diabetic children aged less than 18 years or their care givers in Aseer region. Data were collected from participants using an online pre-structured questionnaire. The questionnaire covered the following data: participants’ socio-demographic data, diabetes related clinical data. The third part covered participants’ knowledge using multiple responses and mutually exclusive questions. The questionnaire was uploaded online using social media platforms by the researchers during the period from 17 November 2021 to 2 June 2022. All accessible and eligible population in the study setting were invited to fill in the attached tool. Results: A total of 499 children fulfilling the inclusion criteria completed the study questionnaire. Exactly 275 (55.1%) were males and 224 (44.9%) were females. None of the sampled children had other co-morbidities. Exactly 188 (37.7%) children had been diagnosed with DM for 6-10 years, 179 (35.9%) diagnosed for 11-15 years while 43 (8.65%) were diagnosed for less than 5 years. About 88% of the study participants agreed that diabetes may cause eye-related complications, and 87.6% agreed that early detection of eye diseases associated with diabetes may reduce their complications. A total of 85.2% think that the annual visit to the ophthalmologist for diabetics is important. Good control of HbA1c as the best method to prevent diabetic eye complications was known by 67.3% of the study participants. Conclusions: In conclusion, the study revealed that diabetic eye complications among children were not frequent with poor diabetic control. Participants’ knowledge regarding diabetes eye related complications was on average especially for the significance of ophthalmologist visits and early detection of the disorders. Keywords: Diabetes, eye complications, children, care givers, knowledge, awareness, Saudi Arabia

Multiple Eye Disease Detection using Hybrid Adaptive Mutation Swarm Optimization and RNN

The major cause of visual impairment in aged people is due to age related eye diseases such as cataract, diabetic retinopathy, and glaucoma. Early detection of eye diseases is necessary for better diagnosis. This paper concentrates on the early identification of various eye disorders such as cataract, diabetic retinopathy, and glaucoma from retinal fundus images. The proposed method focuses on the automated early detection of multiple diseases using hybrid adaptive mutation swarm optimization and regression neural networks (AED-HSR). In the proposed work, the input images are preprocessed and then multiple features such as entropy, mean, color, intensity, standard deviation, and statistics are extracted from the collected data. The extracted features are segmented by using an adaptive mutation swarm optimization (AMSO) algorithm to segment the disease sector from the fundus image. Finally, the features collected are fed to a regression neural network (RNN) classifier to classify each fundus image as normal or abnormal. If the classifier output is abnormal, then it is classified by the corresponding diseases in terms of cataract, glaucoma, and diabetic retinopathy, which improves the accuracy of detection and classification. Ultimately, the results of the classifiers are evaluated by several performance analyses and the viability of structural and functional features is considered. The proposed system predicts the type of the disease with an accuracy of 0.9808, specificity of 0.9934, sensitivity of 0.9803 and F1 score of 0.9861 respectively.

Assignment Flow for Order-Constrained OCT Segmentation

At the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.

Manhattan Vision Screening and Follow-Up Study in Vulnerable Populations: 1-Month Feasibility Results

ABSTRACT Purpose/Aim: In the United States, high rates of vision impairment and eye disease disproportionately impact those who lack access to eye care, specifically vulnerable populations. The objective of our study was to test instruments, implement protocols, and collect preliminary data for a larger 5-year study, which aims to improve detection of eye diseases and follow-up eye care in vulnerable populations using community health workers (CHW) and patient navigators. In the study, trained CHWs conducted vision screening and patient navigators scheduled on-site eye exams and arranged appointments for those referred to ophthalmology to improve adherence to follow-up eye care. Materials and Methods: Eligible individuals age 40-and-older were recruited from the Riverstone Senior Center in Upper Manhattan, New York City. Participants underwent on-site vision screening (visual acuity with correction, intraocular pressure measurements, and fundus photography). Individuals who failed the vision screening were scheduled with an on-site optometrist for an eye exam; those with ocular pathologies were referred to an ophthalmologist. Participants were also administered the National Eye Institute Visual Function Questionnaire-8 (NEI-VFQ-8) and Stopping Elderly Accidents, Deaths, and Injuries (STEADI) test by community health workers. Results:Participants (n = 42) were predominantly older adults, with a mean age of 70.0 ± 9.8, female (61.9%), and Hispanic (78.6%). Most individuals (78.6%, n = 33) failed vision screening. Of those who failed, 84.8% (n = 28) attended the on-site eye exam with the optometrist. Ocular diagnoses: refractive error 13/28 (46.4%), glaucoma/glaucoma suspect 9/28 (32.1%), cataract 7/28 (25.0%), retina abnormalities 6/28 (21.4%); 13 people required eyeglasses. Conclusion: This study demonstrates the feasibility of using CHWs and patient navigators for reducing barriers to vision screening and optometrist-based eye exams in vulnerable populations, ultimately improving early detection of eye disease and linking individuals to additional eye care appointments. The full five-year study aims to further examine these outcomes.

Eye Diseases Classification Using Back Propagation Artificial Neural Network

A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.

Clinical outcomes of the Centre for Eye Health: an intra-professional optometry-led collaborative eye care clinic in Australia

ABSTRACT Clinical relevance: This novel clinical model is the first of its kind in Australia and was designed to help reduce unnecessary referrals into overburdened public systems by utilising pre-existing community-based resources. Background: The Centre for Eye Health (CFEH) is an intra-professional optometry-led care clinic offering an alternative pathway to traditional ophthalmology-based pathways (public hospital clinics or private practices) for ‘at-risk’ patients requiring ocular imaging, diagnostic and management services. This study evaluates the CFEH integrated eye-care model in the identification of chronic eye diseases within the community. Methods: A retrospective random clinical audit of over 750 medical records of patients referred to the CFEH between July 2016 and June 2019 was conducted. Demographics of patients, referral type, final diagnosis and recommended management plans were extracted from this subset. Clinic key performance indicators (referral turnaround time, and net cost per patient appointment) were also extracted. Results: Of the 755 referrals associated with the audited records, 77.4% resulted in the identification of patients with or at-risk of developing eye diseases with 73.5% of this cohort requiring ongoing monitoring at CFEH or referral to ophthalmology. Although the CFEH model is not designed to diagnose or manage acute conditions, 1.5% of patients in this pathway required same day ophthalmological or medical intervention. The cost per patient was equivalent to hospital eye departments costs. Conclusion: This integrated care pathway has the potential to reduce unnecessary referrals from optometrists to hospital ophthalmological service by offering a safe and effective alternate pathway. The majority of patients seen within this pathway were able to be monitored within optometry-led services. This is a unique clinical model utilising inter-professional referrals within optometry which has the potential to reduce preventable blindness within the community through the early detection of eye diseases.

Gold nanoparticles to enhance ophthalmic imaging.

The use of gold nanoparticles as diagnostic tools is burgeoning, especially in the cancer community with a focus on theranostic applications to both cancer diagnosis and treatment. Gold nanoparticles have also demonstrated great potential for use in diagnostic and therapeutic approaches in ophthalmology. Although many ophthalmic imaging modalities are available, there is still a considerable unmet need, in particular for ophthalmic molecular imaging for the early detection of eye disease before morphological changes are more grossly visible. An understanding of how gold nanoparticles are leveraged in other fields could inform new ways they could be utilized in ophthalmology. In this paper, we review current ophthalmic imaging techniques and then identify optical coherence tomography (OCT) and photoacoustic imaging (PAI) as the most promising technologies amenable to the use of gold nanoparticles for molecular imaging. Within this context, the development of gold nanoparticles as OCT and PAI contrast agents are reviewed, with the most recent developments described in detail.

An Intelligent System for Early Detection of Eye Diseases that Lead to Irreversible Vision Loss

An Intelligent System for Early Detection of Eye Diseases that Lead to Irreversible Vision Loss

The capability of early detection of eye diseases in the community by primary health facility worker

Cataract is one of the biggest causes of blindness in the world. As many as 2.4 million people suffered from cataracts in Indonesia in 2013. Therefore, early detection needs to be done to prevent further complications. However, health workers' limited ability in primary health care facilities has resulted in high cataract diagnosis rates. The purpose of this study was to determine the ability of early detection of eye diseases, especially cataracts by primary health care workers. This study was conducted using a quantitative descriptive method analyzing 158 medical records as a result of early detection of primary health care facilities in Sleman Regency, which were sent to the ophthalmologist team of the Department of Eye Health FKKMK UGM to be re-examined. The re-examination results by a team of ophthalmologists received 141 medical records that fulfilled the inclusion criteria, consisting of 61 patients confirmed by cataract diagnosis (43.3%) and 80 patients with a diagnosis of not cataract (56.7%). According to the study, primary health care workers' ability towards early detection of cataracts in Sleman Regency needed to be enhanced (4.3%).

Relationship Between Acuity and Contrast Sensitivity: Differences Due to Eye Disease.

PurposeVisual acuity (VA) and contrast sensitivity (CS) characterize different aspects of visual function. Whereas VA is a standard test in routine eye exams and clinical trials, CS is often not included. We investigated the pathology-specific dissociation between VA and CS by quantifying and comparing the relationship between these two measures in common ocular pathologies.MethodsVA and CS data were assembled from 1113 subjects, including groups with cataract (n = 450), age-related macular degeneration (AMD; n = 232), glaucoma (n = 100), retinitis pigmentosa (RP; n = 87), and normal ocular health (n = 244). VA and CS were measured by the Early Treatment Diabetic Retinopathy Study chart and Pelli–Robson chart, respectively.ResultsEven when VA was relatively normal (<0.3 logMAR), the four ocular pathology groups showed quantitatively different mean CS deficits relative to normal controls, ranging from –0.01 log units for cataract to 0.23 log units for RP. When the entire range of VA was considered, the corresponding deficits in CS were noticeably different across these four groups, being least for cataract and progressively more severe for glaucoma, AMD, and RP. For every 1.0 logMAR loss of VA, the corresponding deficit in CS ranged from 0.22 logCS for cataract to 0.97 logCS for RP.ConclusionsThe quantitative relationship between VA and CS depends on the ocular pathology. CS appears to provide valuable complementary information to VA in the early detection of eye disease and when evaluating visual impairment.

An Optimized Method Using CNN, RF, Cuckoo Search and HOG for Early Detection of Eye Disease in Humans

Glaucoma is a group of eye diseases that cause damage to the optic nerve, causing the successive narrowing of the visual field in affected patients due to increased intraocular pressure, which can lead the patient, at an advanced stage, to blindness without clinical reversal. As we have heard and seen from generations across that Glaucoma has been and is still one of the leading diseases that has permanent damage if untreated. As per the current research it says that 79 Million are affected BY 2020 which are untreated. So, to make it easy for us humans, early detection is one of the best way to create awareness and treat the diseased. After having gone through the majority of the literatures, have seen that when LBP is given to HOG has accurate results for better feature extraction than other methods, also application of Cuckoo search (CS) algorithm, Random forest (for classifying) and Conventional Neural Network (for segmentation) have better outcome compared to the previously used hybrid algorithm methods to detected the diseased from the normal eye. So, to achieve this I will be using Matlab tool as it produces more accurate results than any other platform. In one of the paper LBP algorithm has been extensively used to obtain the desired results but when learnt about HOG, it looked as it has better properties to enhance the required results when combined along with LBP. CS is another unique method to analyze on aggregation of the image texture.