Cholesterol Level Detection Based on Iris Recognition Using Convolutional Neural Network Method

Banowati, Citra; Novianty, Astri; Setianingsih, Casi

doi:10.1109/csudet47057.2019.9214690

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…In [ 31 ], the authors developed a high accuracy (97.45%) convolutional neural network-based Android application that determines cholesterol levels in a person’s body by capturing the image of the iris. A user with high cholesterol levels has a white–greyish circle on the outer circle of the iris.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Δρίτσας

Trigka

2022

Sensors

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Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Δρίτσας

Trigka

2022

Sensors

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“…Banowati et al [7] developed an application that can detect AS using the camera of an Android smartphone. They used pre-trained Inception-v3 architectures with 10-fold crossvalidation fused with epoch, batch size, and learning rate values of 800, 100 and 0.01 respectively.…”

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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“…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]. Most of these methods are based on ML algorithms that extract all the image features before training the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Banowati et al developed an application that can detect CA by using android smartphone camera. They applied pre-trained Inception-v3 architectures with 10-fold cross validation in classifying normal eye and CA images [21]. Putri and Saputro [20] proposed a CNN model consists of 20 hidden layers with four convolution layers to classify LDL level status into two classes (High LDL and Normal LDL).…”

Section: Introductionmentioning

confidence: 99%

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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Cholesterol Level Detection Based on Iris Recognition Using Convolutional Neural Network Method

Cited by 10 publications

References 4 publications

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

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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