Using convolutional neural networks for corneal arcus detection towards familial hypercholesterolemia screening

Kocejko, Tomasz; Rumiński, Jacek; Mazur-Milecka, Magdalena; Romanowska-Kocejko, Marzena; Chlebus, Krzysztof; Jo, Kang-Hyun

doi:10.1016/j.jksuci.2021.09.001

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Traditional methods of identifying CA rely on the interpretation of iris images by a medical professional, which is time-consuming and subject to interpretation discrepancies ( 29 ). To this end, Kocejko et al ( 30 ) designed a mobile application based on a convolutional neural network (CNN) model to identify CA. The training data consisted of 3,900 iris images of various stages of CAs and iris images without CAs, mainly from the University Clinical Centre Gdansk.…”

Section: Resultsmentioning

confidence: 99%

Applications of machine learning in familial hypercholesterolemia

Luo,

Wang,

et al. 2023

Front. Cardiovasc. Med.

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Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.

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Section: Resultsmentioning

confidence: 99%

Applications of machine learning in familial hypercholesterolemia

Luo,

Wang,

et al. 2023

Front. Cardiovasc. Med.

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“…However, these methods require longer time to train and test dataset in order to achieve better accuracy. Moreover, Kocejko et al [8] used CNN, VGG16, Resnet and Inception architecture with 0.0001 initial learning rate using Adam optimizer to automatically detect the presence of AS. The performance of the models was evaluated on a set of images taken by volunteers using a custom mobile application.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Deep Learning Parameters for Arcus Senilis Classification

AH,

Ramlee,

Mas’ud

et al. 2022

JTEC

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Deep learning technique has recently yielded positive results that have increased productivity system for artificial intelligent task, especially the digital image processing and advance machine vision. The popularity of deep learning technique has a major impact on solving complex problems in many fields, particularly the medical science owing to its applications in medical imaging, disease diagnosis, and much more. However, successful application of deep learning depends upon the appropriate setting of the parameters to achieve better result. Therefore, this paper presents a comparative analysis of different base learning rate and batch size configurations for arcus senilis (AS) classification using deep learning techniques. In this analytical study, a dataset of 402 eye images comprising 158 normal and 244 abnormal eye images was employed. Two well-known ResNet-50, VGG-19 and pre-trained convolutional neural network (CNN) models have been trained and validated with 10-fold cross validation using the proposed dataset. Furthermore, base learning rate and batch size were adjusted accordingly to determine the optimal convergence of each model by observing the validation accuracy and error. Experimental result shows that the best combined system has achieved an overall accuracy of 99.78% with a base learning rate of 0.0001 and a batch size of 20 on CNN pre-trained model validation set. Moreover, CNN produces the best result on F1-score and standard deviation of 99.77% and 0.464 respectively. Thus, it can be concluded that CNN requires a considerably smaller number of parameters and reasonable computing time to achieve state-of-the-art performances. This study shows that CNN has the tendency to consistently improve inaccuracy with growing number of epochs, with no signs of overfitting and performance.

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“…The rapid advancement of machine learning (ML) and image oriented deep learning (DL) is enabling for the development of solutions for increasingly complicated challenges. Artificial intelligence (AI) is becoming more appealing for medical applications, enabling for the exploration of new areas where computer algorithms might improve medical operations [9]- [12], also recent world-wide disease namely COVID-19 that has been declared as a pandemic by the World Health Organization on 11 th March 2020 [13]- [16]. Recent studies have demonstrated the potential of various non-invasive techniques in detecting cholesterol from the images of iris [1], [17]- [21], skin [22], [23], MRI [24], and hand pattern [25].…”

Section: Introductionmentioning

confidence: 99%

“…Amini & Ameri applied AlexNet and VGG-16 pre-trained CNN architectures with 4-fold cross-validation into normal and CA images using transfer learning approach [1]. Kocejko et al [9] used CNN automatic identification of the CA presence. On top of that, the author also used neural network models based on the VGG16, ResNet and Inception architectures.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of automatic classification of arcus senilis-nonarcus senilis using convolutional neural networ

Halim

Ramlee

Mas'ud

et al. 2022

IJEECS

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Cholesterol is a type of lipid found in the human body and is susceptible to abnormalities. It can be detected via lipid profiling through blood sampling. In addition, cholesterol can be detected through the presence of a "sodium ring" in the eye iris called the corneal arcus (CA), presenting a new preliminary detection method that is less invasive. Therefore, this paper proposed a non-invasive method in detecting cholesterol based on convolutional neural network (CNN) model representation using 300 normal and 300 abnormal iris images from UBIRIS and medical web images. In this work, contrast-limited adaptive histogram (CLAHE) and unsharp masking process was applied first on CA images to enhance the quality of CA images. To detect the CA images, the dataset was trained and tested using three pre-trained CNN architectures; one is created from scratch, another are Resnet-50 and VGG-19 architectures that were fine-tuned to the CA images. The best result was exhibited by proposed pre-trained CNN model created from scratch with 10-fold cross-validation that produced high average detection accuracy at 98.81%. Thus, deeper network implementation is recommended in the future to further improve CA localization for optometrists used in their daily clinical tasks in detecting cholesterol.

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Using convolutional neural networks for corneal arcus detection towards familial hypercholesterolemia screening

Cited by 4 publications

References 20 publications

Applications of machine learning in familial hypercholesterolemia

Applications of machine learning in familial hypercholesterolemia

A Comparative Study of Deep Learning Parameters for Arcus Senilis Classification

Enhancement of automatic classification of arcus senilis-nonarcus senilis using convolutional neural networ

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