Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Weni, Indra; Utomo, Pradita Eko Prasetyo; Hutabarat, Benedika Ferdian; Alfalah, Muksin

doi:10.22146/ijccs.61882

Cited by 22 publications

(8 citation statements)

References 20 publications

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“…Weni et al [26] conducted research on a CNN-based approach for cataract detection, with a goal to enhance diagnostic accuracy while mitigating loss. The method achieved a classification accuracy of 95% after conducting 50 epochs.…”

Section: Cataract Detection Using Deep Learning Methodsmentioning

confidence: 99%

Enhancing Cataract Detection Precision: A Deep Learning Approach

Yadav,

Yadav

2023

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Cataracts, characterized by the opacification of the eye lens leading to visual deterioration, pose a significant global health issue. Timely and accurate detection of cataracts is pivotal for halting disease progression and augmenting the patients' quality of life. However, conventional diagnostic approaches for cataract detection and grading rely heavily on the expertise of ophthalmologists, a solution that can be unduly costly and inaccessible for certain population segments seeking early intervention. Addressing this challenge, the present study introduces a computer-assisted diagnostic strategy for the detection and grading of cataracts, drawing on fundus retinal images. The proposed approach capitalizes on a deep convolutional neural network to extract features from fundus images, which are subsequently evaluated via three distinct classification algorithms: Support Vector Machine, Naive Bayes, and Decision Tree. The resultant categorization stratifies the images into four severity levels: mild, moderate, normal, and severe. Further enhancing the classifier's prediction accuracy, an Ensemble (ES) learning mechanism via a Majority Voting Scheme (MVS) process is incorporated into the study. A total of 1600 fundus images, sourced from various open-access databases and classified into four categories by an expert ophthalmologist, were utilized for the study. The proposed methodology demonstrated a commendable accuracy rate of 97.34% in the four-stage cataract classification and grading, outperforming existing methodologies. This research advances the field by introducing a reliable, cost-effective, and accessible solution for early cataract detection, contributing significantly to global health improvements.

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Section: Cataract Detection Using Deep Learning Methodsmentioning

confidence: 99%

Enhancing Cataract Detection Precision: A Deep Learning Approach

Yadav,

Yadav

2023

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“…The following section provides a comprehensive literature review on the subject matter, examining relevant studies and scholarly works. Weni et al [7] proposed a CNN-based system for cataract classification achieving an accuracy of 97% in 50 epochs using a basic CNN architecture with ReLU and SoftMax activation. Hossain et al [8] developed a ResNet50based method for cataract detection in fundus images.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Study of DCNN Algorithms-based Transfer Learning for Human Eye Cataract Detection

Jidan,

Paul,

Roy

et al. 2023

IJACSA

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This study presents a comparative analysis of different deep convolutional neural network (DCNN) architectures, including VGG19, NASNet, ResNet50, and MobileNetV2, with and without data augmentation, for the automatic detection of cataracts in fundus images. Utilizing hybrid architecture models, namely ResNet50-NASNet and ResNet50+MobileNetV2, which combine two state-of-the-art DCNNs, this research demonstrates their superior performance. Specifically, MobileNetV2 and the combined ResNet50+MobileNetV2 outperform other models, achieving an impressive accuracy of 99.00%. By emphasizing the efficacy of diverse datasets and pre-processing techniques, as well as the potential of pretrained DCNN models, this study contributes to accurate cataract diagnosis. Furthermore, the proposed system has the potential to reduce reliance on ophthalmologists, decrease the cost of eye check-ups, and improve accessibility to eye care for a wider population. These findings showcase the successful application of deep learning and image processing techniques in the early detection and treatment of various medical conditions, including cataracts, addressing the needs of individuals with diminished vision through ocular images and innovative hybrid architectures.

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“…Then, for optimization of the fullyconnected layer on CNN, binary crossentropy is used as a loss function of the data parameters. Accuracy is a description of the model's reliability in accurately classifying the model [29]. Precision compares the accuracy of the data presented with the model's predictions [16].…”

Section: Preprocessingmentioning

confidence: 99%

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Ichsan

Armita

Minarno

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

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Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Cited by 22 publications

References 20 publications

Enhancing Cataract Detection Precision: A Deep Learning Approach

Enhancing Cataract Detection Precision: A Deep Learning Approach

A Comprehensive Study of DCNN Algorithms-based Transfer Learning for Human Eye Cataract Detection

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

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