Implementation of support tools for the presumptive diagnosis of Glaucoma through identification and processing of medical images of the human eye

Pinos-Velez, Eduardo; Flores-Rivera, Maria; Ipanque-Alama, William; Herrera-Alvarez, Deysi; Chacon, CarlosLuis; Serpa-Andrade, Luis

doi:10.1109/syseng.2018.8544409

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…They also used a polar change, which converts the original fundus image into the polar direction framework to further enhance the division outcome. Eduardo Pinos-Velez, María Flores-Rivera [12] have implemented the ancillary tools for identifying and analysing human eye medical pictures to help in the provisional diagnosis of glaucoma. They focused on biomedical image processing to identify the characteristics regarded most significant within pictures collected from the back of the eye in order to diagnose glaucoma, a disorder that predominantly affects the physical statistics of the cup and the optical disc.…”

Section: Related Studymentioning

confidence: 99%

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Glaucoma Detection Using Explainable AI and Deep Learning

Afreen,

Aluvalu

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed. OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection. RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS). CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

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Section: Related Studymentioning

confidence: 99%

“…In [12][20] [40][41] [42][44] [48][50] [71] the authors emphasize on the main feature for the detection of glaucoma is CDR i.e., the ratio between the cup and the disc and the thickness of optic nerve.…”

Section: Feature Extractionmentioning

confidence: 99%

Glaucoma Detection Using Explainable AI and Deep Learning

Afreen,

Aluvalu

2024

EAI Endorsed Trans Perv Health Tech

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“…Then they set up the epochs to 200 with the 10-fold cross validation and achieved accuracy of 0.78. Pinos-Velez et al [24] diagnosed glaucoma by the using of ISNT rule. In a normal eye CDR ratio is below 0.3.…”

Section: Problem Statementmentioning

confidence: 99%

Glaucoma Detection Using Inception Convolutional Neural Network V3

Afroze

Akther

Chowdhury

et al. 2021

Communications in Computer and Information Science

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Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.

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“…As preprocessing authors applied 2D median filter and Multi threshold methods. Researches [38][39][40][41][42][43] state that the majority of classical CDR based approaches suffer inaccuracy in prediction, especially in OC detection over the horizontally identified disk region that makes overall detection output suspicious. In [45], authors pre-processed input with filtering, green channel extraction and CLAHE implementation to achieve better CDR estimation.…”

Section: Related Workmentioning

confidence: 99%

GlaucoNet: A Highly Robust Stacked Auto Encoder assisted Deep Learning Model for Glaucoma Detection System

Patil*,

Rao

2019

IJEAT

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High pace rise in Glaucoma, an irreversible eye disease that deteriorates vision capacity of human has alarmed academia-industries to develop a novel and robust Computer Aided Diagnosis (CAD) system for early Glaucomatic eye detection. The main root cause for glaucoma growth depends on its structural alterations in the retina and is very much essential for ophthalmologists to identify it at an initial period to stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Recently, numerous efforts have been made to exploit SpatialTemporal features including morphological values of Optical Disk (OD), Optical Cup (OC), Neuro-Retinal Rim (NRR) etc to perform Glaucoma detection in fundus images. Here, some issues like: suitable pre-processing, precise Region of Interest segmentation, post-segmentation and lack of generalized threshold limits efficacy of the major existing approaches. Furthermore, the optimal segmentation of OD and OC, nerves removal from OD or OC is often tedious and demands more efficient solution. However, these approaches cumulatively turn out to be computationally complex and time-consuming. As potential alternative, deep learning techniques have gained widespread attention, especially for image analysis or vision technologies. With this motive, in this paper, the authors proposed a novel Convolutional Stacked Auto-Encoder (CSAE) assisted Deep Learning Model for Glaucoma Detection and Classification model named GlaucoNet. Unlike classical methods, GlaucoNet applies Stacked Auto-Encoder by using hierarchical CNN structure to perform deep feature extraction and learning. By adapting complex data nature, and large features, GlaucoNet was designed with three layers: convolutional layer (CONV), Max-pool layer (MP) and two Fully Connected (FC) layers where the first performs feature extraction and learning, while second exhibits feature selection followed by the reduction of spatial resolution of the individual feature map to avoid large number of parameters and computational complexities. To avoid saturation problem in this work, by marking an applied dropout as 0.5. MATLAB based simulation-results with DRISHTI-GS and DRION-DB datasets affirmed that the proposed GlaucoNet model outperforms as compared to other state-of-art techniques: neural network based approaches in terms of accuracy, recall, precision, F-Measure and balanced accuracy. The overall parametric measured values shown better performance for GlaucoNet model.

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Implementation of support tools for the presumptive diagnosis of Glaucoma through identification and processing of medical images of the human eye

Cited by 7 publications

References 3 publications

Glaucoma Detection Using Explainable AI and Deep Learning

Glaucoma Detection Using Explainable AI and Deep Learning

Glaucoma Detection Using Inception Convolutional Neural Network V3

GlaucoNet: A Highly Robust Stacked Auto Encoder assisted Deep Learning Model for Glaucoma Detection System

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