Automatic Classification of Pterygium-Non Pterygium Images Using Deep Learning

Lopez, Yadini Perez; Aguilera, Luis Rojas

doi:10.1007/978-3-030-32040-9_40

Cited by 7 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They analyzed several configurations of network regularization, which included local response normalization, batch normalization, and dropout. Their best accuracy of 98.33% was obtained by using embedded local response normalization and dropout layers, which is a significant improvement compared to Lopez and Aquilera's [34] accuracy of 93.5%.…”

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 86%

“…In summary, the work by Lopez and Aquilera [34] has a clear weakness in terms of the quality of the extracted features due to the utilization of a single CNN layer only. On the flip side, it has a very low computational requirement due to the compact architecture with a low number of parameters.…”

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 99%

“…Hence, in the case of pterygium screening and diagnosis, the input image will be passed to the networks, whereby three problems of interest will be analyzed, which are pterygium classification, pterygium tissue localization, and pterygium tissue semantic segmentation. During the early adoption of deep learning techniques for pterygium screening, the network was designed to be compact in nature [34,35]. In [34], Lopez and Aquilera designed compact deep learning networks with only a single layer of convolutional neural networks (CNN) and two dense feed-forward layers.…”

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 99%

“…During the early adoption of deep learning techniques for pterygium screening, the network was designed to be compact in nature [34,35]. In [34], Lopez and Aquilera designed compact deep learning networks with only a single layer of convolutional neural networks (CNN) and two dense feed-forward layers. The network was down-pooled and flattened before being passed to the dense classifier.…”

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 99%

See 3 more Smart Citations

Computer-Assisted Pterygium Screening System: A Review

et al. 2022

View full text Add to dashboard Cite

Pterygium is an eye condition that causes the fibrovascular tissues to grow towards the corneal region. At the early stage, it is not a harmful condition, except for slight discomfort for the patients. However, it will start to affect the eyesight of the patient once the tissues encroach towards the corneal region, with a more serious impact if it has grown into the pupil region. Therefore, this condition needs to be identified as early as possible to halt its growth, with the use of simple eye drops and sunglasses. One of the associated risk factors for this condition is a low educational level, which explains the reason that the majority of the patients are not aware of this condition. Hence, it is important to develop an automated pterygium screening system based on simple imaging modalities such as a mobile phone camera so that it can be assessed by many people. During the early stage of automated pterygium screening system development, conventional machine learning techniques such as support vector machines and artificial neural networks are the de facto algorithms to detect the presence of pterygium tissues. However, with the arrival of the deep learning era, coupled with the availability of large training data, deep learning networks have replaced the conventional networks in screening for the pterygium condition. The deep learning networks have been successfully implemented for three major purposes, which are to classify an image regarding whether there is the presence of pterygium tissues or not, to localize the lesion tissues through object detection methodology, and to semantically segment the lesion tissues at the pixel level. This review paper summarizes the type, severity, risk factors, and existing state-of-the-art technology in automated pterygium screening systems. A few available datasets are also discussed in this paper for both classification and segmentation tasks. In conclusion, a computer-assisted pterygium screening system will benefit many people all over the world, especially in alerting them to the possibility of having this condition so that preventive actions can be advised at an early stage.

show abstract

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 86%

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 99%

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 99%

Section: Deep Learning Approach To Automated Pterygium Systemmentioning

confidence: 99%

See 2 more Smart Citations

Computer-Assisted Pterygium Screening System: A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The previously discussed methods have applied simple image processing methods without using advanced supervision techniques. The work by Lopez et al [ 19 ] adopted a convolutional neural network (CNN) to classify the eye images into a pterygium or normal class. The network is very shallow with just one layer of CNN and one dense layer with one down-pooling operator.…”

Section: Related Workmentioning

confidence: 99%

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Abdani

Zulkifley

2021

Diagnostics

View full text Add to dashboard Cite

Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.

show abstract

Group and Shuffle Convolutions for High-Resolution Semantic Segmentation

Abdani

Zulkifley

2021

2021 International Conference on Decision Aid Sciences and Application (DASA)

View full text Add to dashboard Cite

Automatic Classification of Pterygium-Non Pterygium Images Using Deep Learning

Cited by 7 publications

References 5 publications

Computer-Assisted Pterygium Screening System: A Review

Computer-Assisted Pterygium Screening System: A Review

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Group and Shuffle Convolutions for High-Resolution Semantic Segmentation

Contact Info

Product

Resources

About