Retinal Markers for Early Detection of Eye Disease

The “red reflex test” is used to screen children for leukocoria (“white eye”) in a standard pediatric examination, but is ineffective at detecting many eye disorders. Leukocoria also presents in casual photographs. The clinical utility of screening photographs for leukocoria is unreported. Here, a free smartphone application (CRADLE: ComputeR-Assisted Detector of LEukocoria) was engineered to detect photographic leukocoria and is available for download under the name “White Eye Detector.” This study determined the sensitivity, specificity, and accuracy of CRADLE by retrospectively analyzing 52,982 longitudinal photographs of children, collected by parents before enrollment in this study. The cohort included 20 children with retinoblastoma, Coats’ disease, cataract, amblyopia, or hyperopia and 20 control children. For 80% of children with eye disorders, the application detected leukocoria in photographs taken before diagnosis by 1.3 years (95% confidence interval, 0.4 to 2.3 years). The CRADLE application allows parents to augment clinical leukocoria screening with photography.

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Early Detection of Diabetic Eye Disease through Deep Learning using Fundus Images

EAI Endorsed Transactions on Pervasive Health and Technology ◽

10.4108/eai.13-7-2018.164588 ◽

2020 ◽

Vol 6 (22) ◽

pp. 164588

Author(s):

Rubina Sarki ◽

Khandakar Ahmed ◽

Yanchun Zhang

Keyword(s):

Deep Learning ◽

Early Detection ◽

Eye Disease ◽

Fundus Images ◽

Diabetic Eye Disease

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Cup and Disc Ratio and Inferior, Superior, Temporal and Nasal Calculation for Glaucoma Identification

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2720 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1316-1319

Author(s):

K. Manju ◽

R. S. Sabeenian

Keyword(s):

Early Detection ◽

Vision Loss ◽

Eye Disease ◽

Disc Diameter ◽

Cup To Disc Ratio

Glaucoma is the deadliest eye disease that steals the vision. Glaucoma is recorded as the second reason for vision loss. When identified it would have created 25% damage to the tissues and 50% vision loss. This has been so successful in stealing the vision because early detection of this eye disease is very difficult. This has catered the researchers. Early detection of glaucoma has always been challenging. In this paper, glaucoma is identified by checking two conditions namely Cup and Disc Ratio (CDR) and ISTN (Inferior, Superior, Temporal and Nasal) criterion. Both conditions must be satisfied to be glaucomatous. Vertical cup and disc diameter is obtained by Otsu threshold segmenting method. The cup to disc ratio is obtained by dividing the vertical disc to vertical cup diameter. ISTN is the criterion that ophthalmologist see to confirm glaucoma. The inferior region should have the greater thickness compared to other regions. A person is named a suspect of glaucoma if the CDR is above 0.6 and inferior region thickness is less than 8-pixel distance. Neural retinal rim thickness can also be taken for further validation.

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Retinal Markers for Early Detection of Eye Disease

Automated Image Detection of Retinal Pathology ◽

10.1201/9781420037005-8 ◽

2009 ◽

pp. 139-172

Keyword(s):

Early Detection ◽

Eye Disease

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

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.2.202 ◽

2020 ◽

Vol 4 (2) ◽

Author(s):

Tian Jipeng ◽

Manasa S. ◽

T. C. Manjunath

Keyword(s):

Neural Network ◽

Early Detection ◽

Hybrid Algorithm ◽

Cuckoo Search ◽

Eye Diseases ◽

Image Texture ◽

Eye Disease ◽

Advanced Stage ◽

Permanent Damage ◽

Unique Method

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.

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ANALISIS TEKSTUR PADA CITRA IRIS MATA MENGGUNAKAN ALGORITMA GRAY LEVEL CO-OCCURENCY MATRIX

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.680 ◽

2019 ◽

Vol 15 (2) ◽

pp. 183-188

Author(s):

Asti Herliana ◽

Toni Arifin

Keyword(s):

Feature Extraction ◽

Diabetic Retinopathy ◽

Early Detection ◽

Correlation Energy ◽

Process Analysis ◽

Eye Disease ◽

Gray Level ◽

Disease Effect ◽

Preprocessing Technique ◽

Process Detection

According to data from the ministry of health, with the high intensity of use the gadget nowadays, therefore the number of people with eye disease is increasing. To overcome increase suffers of eye disease, it takes need early detection for who suffers potentially eye disease so that handling and prevention of blindness from eye disease effect can be immediately. The process detection of eye disease can be see in iris, there are several disease can be seen in iris among there are diabetic retinopathy and glaucoma. This research present texture analysis for iris images, the method is used GLCM (Gray Level Co-occurency Matrix) which is implemented using Matlab, and using 5 parameters namely contrast, correlation, energy, homogeneity and entropy. Process analysis texture is developed with preprocessing technique, the result of texture in images data iris can be recognized and produce the dataset of result from feature extraction with GLCM (Gray Level Co-occurency Matrix).

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Early Detection of Diabetic Eye Disease from Fundus Images with Deep Learning

Lecture Notes in Computer Science - Databases Theory and Applications ◽

10.1007/978-3-030-39469-1_20 ◽

2020 ◽

pp. 234-241

Author(s):

Rubina Sarki ◽

Khandakar Ahmed ◽

Hua Wang ◽

Sandra Michalska ◽

Yanchun Zhang

Keyword(s):

Deep Learning ◽

Early Detection ◽

Eye Disease ◽

Fundus Images ◽

Diabetic Eye Disease

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Glaucoma Detection Using Convolutional Neural Networks

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning - Advances in Medical Diagnosis, Treatment, and Care ◽

10.4018/978-1-7998-2742-9.ch001 ◽

2021 ◽

pp. 1-7

Author(s):

Meet Ganpatlal Oza ◽

Geeta Rani ◽

Vijaypal Singh Dhaka

Keyword(s):

Neural Network ◽

Neural Networks ◽

Intraocular Pressure ◽

Early Detection ◽

Early Stage ◽

Physical Activities ◽

Eye Disease ◽

Eye Injuries ◽

Glaucoma Detection ◽

Ict Tools

The increase in use of ICT tools and decrease in physical activities has increased the risk of disorders such as diabetes, hypertension, myopia, hypermetropia, etc. These disorders make the person more prone to eye disease such as glaucoma. The actual causes of glaucoma are still unknown. But the study of medical literature reveals that the factors such as intraocular pressure, thyroid, diabetics, eye injuries, eye surgeries, ethnic background, and myopia makes the person more prone to glaucoma. The difficulty in early detection make it an invisible thief of sight. Therefore, it is the demand of the day to design a system for its early detection. The aim of this chapter is to develop a convolutional neural network model “GlaucomaDetector” for detection of glaucoma at an early stage. The evaluation of the model on the publicly available dataset reports the accuracy of 99% for prediction of glaucoma from the input images of retina. This may prove a useful tool for doctors for quick prediction of glaucoma at an early stage. Thus, it can minimize the risk of blindness in patients.

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