Automatic Detection of Eye Cataract using Deep Convolution Neural Networks (DCNNs)

Hossain, Md. Rajib; Afroze, Sadia; Siddique, Nazmul; Hoque, Mohammed Moshiul

doi:10.1109/tensymp50017.2020.9231045

Cited by 41 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hossain et al authored a paper entitled “Automatic Detection of Eye Cataract using Deep Convolution Neural Networks.” In his work, he used Deep Convolution Neural Networks, and the module was ResNet50 to detect cataracts and noncataract fundus. Their overall validation accuracy was 97.38% where their training accuracy was nearly 100% [ 15 ]. Li et al introduced a ResNet-based discrete state transition (DST) system.…”

Section: Introductionmentioning

confidence: 99%

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

Hasan

Tanha

Amin

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

Hasan

Tanha

Amin

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Hossain and Afroze [41] proposed an automatic cataract detection system using DCNNs and a trained classifier model based on Res-Net, whose accuracy was 95.77%. Recently, Zhang et al [42] have provided an attention-based Multi-Model Ensemble method for automatic cataract detection on ultrasound images, which, to the best knowledge of the authors, obtained the highest accuracy (97.5%) among the other deep learning-based approaches in the literature.…”

Section: B Deep Learning-based Methodsmentioning

confidence: 99%

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Junayed

Islam²,

Sadeghzadeh

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images were collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

show abstract

“…For Cataract detection, we have studied [3][4][5] in depth. In [3], they have developed an automated cataract grading system through balancing lumination problems in the dataset and building an eight-layered Deep Convolutional Neural Network (DCNN) model.…”

Section: Related Workmentioning

confidence: 99%

RetinalNet-500: A newly developed CNN Model for Eye Disease Detection

Toki

Rahman

Fahim

et al. 2022

2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)

View full text Add to dashboard Cite

In order to successfully complete an undergraduate thesis, it is imperative that the thesis adheres strictly to the rules and regulations of the university, as well as ethical principles for conducting research. We have incorporated original data into our thesis. We have carefully checked our references and citations. The four coauthors of the paper all acknowledge responsibility for any violation of the thesis rule. In order to solve problems we used several conference paper, journal paper, and Coursera/EdX videos. As a result, we'd like to express our appreciation to everyone who has helped us throughout the journey. None of the unethical practices have been used in the completion of our thesis. This has been conducted in accordance with the BRAC university's ethics code.

show abstract

Automatic Detection of Eye Cataract using Deep Convolution Neural Networks (DCNNs)

Cited by 41 publications

References 16 publications

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

RetinalNet-500: A newly developed CNN Model for Eye Disease Detection

Contact Info

Product

Resources

About