Deep learning approach for classification of eye diseases based on color fundus images

Triwijoyo, Bambang Krismono; Sabarguna, Boy Subirosa; Budiharto, Widodo; Abdurachman, Edi

doi:10.1016/b978-0-12-817440-1.00002-4

Cited by 21 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 3 offers a detailed comparative study of the results obtained by the presented and existing techniques [19][20][21][22][23][24]. From the table, it can be seen that the presented techniques have shown better results compared to the other methods.…”

Section: Resultsmentioning

confidence: 98%

Eye Disease Diagnosis and Classification using Deep Convolution Neural Network

Dr. N. Rajkumar, Dr. S. Megala, S. Muthukumaran

2023

tjjpt

View full text Add to dashboard Cite

Human eye plays a vital role in everyone’s life. If retinal diseases occur it could cause complete or partial vision loss. Objective: To overcome that situation, the fundus images can be utilized by the ophthalmologists to automatically diagnose the retinal diseases. At the same time, the development of latest Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models finds useful for object detection in several application areas, including medical imaging. Methods: In this view, this paper presents an automated DL based multi-retinal disease diagnosis and classification model. The presented technique comprises three processes, such as pre-processing, feature extraction, and classification. For feature extraction purposes, AlexNet and Residual Network (ResNet) models are employed. Besides, Convolutional Neural Network (CNN) and Deep Neural Network (DNN) models are applied to classify the retinal fundus images into different retinal diseases. The incorporation of DL models for feature extraction and classification resulted to enhanced diagnostic performance. Findings: A series of experiments were conducted on benchmark dataset and the experimental results are examined under different aspects. The experimental values exhibited that the ResNet-CNN model has achieved better performance with the accuracy of 98.84%, precision of 98.84%, recall of 98.17%, and F1-score of 98.48%. Novelty: With the help of such methods and techniques those latest findings indicate that some human eye diseases which can be alleviated by pharmacological interventions in an easy manner.

show abstract

Section: Resultsmentioning

confidence: 98%

Eye Disease Diagnosis and Classification using Deep Convolution Neural Network

Dr. N. Rajkumar, Dr. S. Megala, S. Muthukumaran

2023

tjjpt

View full text Add to dashboard Cite

show abstract

“…They have become a state-of-art technique in various medical fields. CNNs generally consist of three types of layers [15]: i) Convolutional layers, where a number of filters are applied to identify a certain feature or pattern in the inputted image. A stack of filters is used in order to extract various features.…”

Section: Cnn Overviewmentioning

confidence: 99%

“…Therefore, researchers are intensively relying on transfer learning (TL) to overcome the obstacles of computational time and the need for ongoing training. TL is a method of overcoming the limitedness of data by leveraging knowledge from another domain [15].…”

Section: The Need For Transfer Learningmentioning

confidence: 99%

ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy

Al-Moosawi‬‏

Khudeyer

2021

IJCAI

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is an eye complication associated with diabetes, resulting in blurred vision or blindness. The early diagnosis and treatment of DR can decrease the risk of vision loss dramatically.However, such diagnosis is a tedious and complicated task due to the variability of retinal changes across the stages of the diseases, and due to the high number of undiagnosed and untreated DR cases. In this paper, we develop a computationally efficient and scalable deep learning model using convolutional neural networks (CNN), for diagnosing DR automatically. Various preprocessing algorithms are utilized to improve accuracy, and a transfer learning strategy is adopted to speed up the process. Our experiment used the fundus image set available on online Kaggle datasets. As an ultimate conclusion of applicable performance metrics, our computational simulation achieved a relatively-high F1 score of 93.2% for stage-based DR classification. Povzetek: Opisana je metoda globokih nevronskih mrež za diagnozo težav vida zaradi sladkorne bolezni.

show abstract

“…Some examples of ophthalmic disease detections that have employed deep learning are diabetic retinopathy [24,25], age-related macular degeneration (AMD) [26] and glaucoma [27]. Triwijoyo et al [28] implemented convolutional neural network (CNN) as classifiers for retinal images with the highest accuracy of 80.93%. They are using the STARE dataset, which consists of 15 classes of retinal eye diseases.…”

Section: Related Workmentioning

confidence: 99%

A Mobile Solution for Lateral Segment Photographed Images Based Deep Keratoconus Screening Method

W Zaki,

Mat Daud,

Hudaibah Saad

et al. 2021

IJIE

View full text Add to dashboard Cite

Keratoconus (KC) is a condition of the bulging of the eye cornea. It is a common non-inflammatory ocular disorder that affects mostly the younger populace below the age of 30. The eye cornea bulges because of the conical displacement of either outwards or downwards. Such condition can greatly reduce one’s visual ability. Therefore, in this paper, we afford a mobile solution to mitigate the KC disorder using the state-of-the-art deep transfer learning method. We intend to use the pre-trained VGGNet-16 model and a conventional convolutional neural network to detect KC automatically. The experimental work uses a total of 4000 side view lateral segment photographed images (LSPIs) comprising 2000 ofKC and non-KC or healthy each involving 125 subjects. The LSPIs were extracted from the video data captured using a smartphone. Fine tuning of three hyperparameters namely the learning rate (LR), batch size (BS) and epoch number (EN) were carried out during the training phase to generate the best model of which, the VGGNet-16 model fulfilled it. For the KC detection task, our proposed model achieves an accuracy of 95.75%, a sensitivity of 92.25%, and specificity of 99.25% using the LR, BS and EN of 0.0001,16, and 70, respectively. These results confirmed the high potential of our proposed solution to apprehend KC prevalence towards an automated KC screening procedure.

show abstract

Deep learning approach for classification of eye diseases based on color fundus images

Cited by 21 publications

References 32 publications

Eye Disease Diagnosis and Classification using Deep Convolution Neural Network

Eye Disease Diagnosis and Classification using Deep Convolution Neural Network

ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy

A Mobile Solution for Lateral Segment Photographed Images Based Deep Keratoconus Screening Method

Contact Info

Product

Resources

About