An algorithm for measuring pterygium's progress in already diagnosed eyes

Mesquita, Rafael; Figueiredo, Elliackin

doi:10.1109/icassp.2012.6287988

Cited by 13 publications

(12 citation statements)

References 6 publications

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“…Another study conducted by Gao et al [65] applied Daugman's algorithm in retro-illumination images to detect pterygium and achieved 85.38% accuracy. In addition, early detection of pterygium disease using adaptive sigmoid enhancement techniques performed well on the ASPI database collected from [64]. This technique was successful in detecting pterygium with 93.53% accuracy and 88.18% specificity [8].…”

Section: ) Towards Automated Detectionmentioning

confidence: 88%

“…Mesquita and Figueiredo have applied the Circular Hough technique (CHT) and the Otsu thresholding method to detect the advancement of pterygium tissue in the ASPIs of the eyes [64]. Their proposed method achieved 63.4% accuracy and proved that the DIP approach could be used to investigate pterygium cases.…”

Section: ) Towards Automated Detectionmentioning

confidence: 99%

“…During the data collection process, subjects were seated on an adjustable chair with their eye, forehead and chin placed towards the chinrest. A nonlocal database consisting of 66 pterygium ASPIs contributed by [64] from the Centre of Informatics, Federal University of Pernambuco, Recife Brazil and 66 normal ASPIs were from an online database UBIRIS [75], [76] were also used in this study.…”

Section: A Data Acquisitionmentioning

confidence: 99%

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Automated Pterygium Detection Using Deep Neural Network

et al. 2020

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Ocular imaging has developed rapidly and plays a critical role in clinical care and ocular disease management. Development of image processing technologies pertinent to ocular diseases has paved the way for automated diagnostic systems including detection techniques using deep learning (DL) approaches. The prevalence of an abnormal tissue layer in the conjunctiva, known as pterygium eye disease, is increasing due to lack of awareness. Despite the non-cancerous/benign nature of pterygium, a clinical diagnosis from an ophthalmologist is still required to prevent the pterygium tissues from extending into the pupil, which would result in blurred vision. However, current diagnostic methods are mostly dependent on human expertise. Automated detection can potentially serve as an assistive method to reduce diagnosis time by applying a DL approach. Considering the lack of comprehensive research work on pterygium detection using DL, we propose a new architecture consisting of an improved CNN-based trained network named VggNet16-wbn that is derived from VggNet16, a pre-trained CNN algorithm. This paper presents an overview of the DL as a core approach to the transfer learning (TL) concept, as well as current efforts towards automated ocular detection approaches. A new architecture of a CNN-based trained network was proposed based on a network assessment from six CNN pre-trained networks to detect pterygium. This work consists of two main modules, namely, data acquisition and DCNN classification. The proposed trained network, VggNet16-wbn, shows the best performance with 99.22% accuracy, 98.45% sensitivity, and a perfect score on specificity and area under the curve metrics. This work has high potential for creating a pterygium screening system that can be used as a baseline for fully automated detection using a DL approach.

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Section: ) Towards Automated Detectionmentioning

confidence: 88%

Section: ) Towards Automated Detectionmentioning

confidence: 99%

Section: A Data Acquisitionmentioning

confidence: 99%

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Automated Pterygium Detection Using Deep Neural Network

et al. 2020

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“…The proposed system can overcome the lack of quantitative tools for pterygium screening. The investigation conducted by [11] also supported the use of image processing-based systems to examine the presence of pterygium. However, several challenges, including image variations, illumination imbalances due to the use of low-resolution cameras and variations in pterygium tissue form, needed be addressed in the development of these systems.…”

Section: Introductionmentioning

confidence: 84%

“…A total of 844 pterygium and normal anterior segment photographed images (ASPI) were extracted from myMata.my database, which has been acquired by researchers at the Centre of Integrated Systems and Advanced Technologies (INTEGRA) in Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia. The database consisting of ASPIs were collected locally and from the online sources [11], [25], [26]. All local ASPIs have been verified by experts from the Eye Clinic, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, whereas the ground truth of the ASPIs obtained from online sources were provided.…”

Section: Aspi Databasementioning

confidence: 99%

Automated Pterygium Detection in Anterior Segment Photographed Images using Deep Convolutional Neural Network

Saad¹

2019

IJATCSE

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This paper presents an automated detection of pterygium disease in anterior segmented photographed images. The five main steps are implemented as follows; pre-processing of the ASPI database, model development using a proposed deep convolutional neural network (DCNN) architecture, evaluation of the tested models, analysis of the models' efficiencies and comparison of model performance. The proposed DCNN architecture consists of six learned layers that are three convolutional and three fully connected layers. This experimental work focuses on the identification of the best combination of hyperparameters for the proposed DCNN architecture. The optimal hyperparameter combination with learning rates of 0.001 and 0.0001 and epoch values of 20 and 25, provides the best performance of 94.09% accuracy, 93.93% F1 score, and 88.32% Matthew correlation coefficient with the highest area under the curve value for an average fold of 95%. The results prove that the proposed DCNN architecture has a promising potential to be applied in the development of future pterygium screening tools.

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Automatic Classification of Pterygium-Non Pterygium Images Using Deep Learning

Lopez¹,

Aguilera²

2019

VipIMAGE 2019

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An algorithm for measuring pterygium's progress in already diagnosed eyes

Cited by 13 publications

References 6 publications

Automated Pterygium Detection Using Deep Neural Network

Automated Pterygium Detection Using Deep Neural Network

Automated Pterygium Detection in Anterior Segment Photographed Images using Deep Convolutional Neural Network

Automatic Classification of Pterygium-Non Pterygium Images Using Deep Learning

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