Classification of Retinal Diseases from OCT scans using Convolutional Neural Networks

Najeeb, Suhail; Sharmile, Nowshin; Khan, Md. Sajid; Sahin, Ipsita; Islam, Mohammad Tariqul; Bhuiyan, Mohammed Imamul Hassan

doi:10.1109/icece.2018.8636699

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…It adds four customized layers: a dropout layer, a fully connected layer, a softmax layer, and a classification layer. The dropout layer is added with a probability of 50% to prevent the overfitting of the network 5 . The fully connected layer is added with a weight learn rate factor of 10 and a bias learn rate factor of 10 to consider all kernel outputs in the calculation of the class probability by the softmax layer 24 .…”

Section: Methodsmentioning

confidence: 99%

“…Based on local and global feature extraction, the model achieved an accuracy of 90.65%. The deep learning approach using the CNN technique was proposed by Najeeb et al 4 The study aimed to classify the most common retinal diseases. The study focused on the extraction of a region of interest from OCT images.…”

Section: Related Workmentioning

confidence: 99%

“…The drusen case expresses a change of color (white or yellow) of the region between Bruch's membrane and the retinal pigment epithelium of the eye. 4 Recently, the computeraided diagnosis (CAD) approach has been widely applied in medical decision-making. The idea is to create an automated expert system using artificial intelligence (AI) techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The DME is the presence of fluid beneath the edema of the eye, which makes the retina thicker and more swell than a normal eye. The drusen case expresses a change of color (white or yellow) of the region between Bruch's membrane and the retinal pigment epithelium of the eye 4 . Recently, the computer‐aided diagnosis (CAD) approach has been widely applied in medical decision‐making.…”

Section: Introductionmentioning

confidence: 99%

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Transfer learning‐based platform for detecting multi‐classification retinal disorders using optical coherence tomography images

Saleh

Wahed

Salaheldin

2021

Int J Imaging Syst Tech

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One of the primary concerns of computer‐aided diagnosis is the detection of retinal disorders. The study aims to categorize the patients into choroidal neovascularization, diabetic macular edema, drusen, and normal by using optical coherence tomography (OCT) images. For the first time, two novel transfer learning‐based techniques were used for retinal disorder classification: SqueezeNet and the Inception V3 Net. Two SqueezeNet scenarios were used to compare the performance of the original SqueezeNet and the improved one. A dataset of 11 200 OCT images was used for data partitioning of SqueezeNet and, meanwhile, 18 000 images for Inception V3 Net. The modified SqueezeNet achieved 98% accuracy, a 1.2% improvement over the original. The Inception V3 Net classifier improved its classification accuracy to 98.4%. When compared to other classifiers and a human expert, the transfer learning approach demonstrated its robustness in the challenge of retinal disorders classification with a large dataset.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transfer learning‐based platform for detecting multi‐classification retinal disorders using optical coherence tomography images

Saleh

Wahed

Salaheldin

2021

Int J Imaging Syst Tech

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“…The proposed technique with Random Forest has acquired the best performances with accuracy of 99.78% and sensitivity 99.8% via Kermany et al's dataset (Kermany et al, 2018) . Najeeb et al (2019) presented an image pre-processing algorithm to acquire the region of interest (ROIs) from retinal OCT images and a single layer convolutional neural network structure to detect retinal diseases from segmented OCT images. They used the OCT dataset containing four classes published by Zhang Lab at the University of California at San Diego (UCSD) (Kermany et.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

2022

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The retina is an eye layer that incorporates light- and color-sensitive cells as well as nerve fibers. It collects light and distributes it to the brain for image processing through the use of the optic nerve. Diseases that end up causing vision loss and blindness are generated by retinal ailments. As a result, it is imperative to diagnose and treat certain disorders as early as possible. Optical coherence tomography (OCT), an angiography imaging technique, is operated to help diagnose retinal disorders. Deep learning approaches, which are extensively utilized, have now become a convenient way for diagnosing retinal illnesses through OCT images as a result of their effective outcomes in interpreting medical images. To diagnose retinal disorders utilizing OCT scans, this investigation developed a hybrid methodology based on image pre-processing and convolutional neural networks (CNNs) (a deep learning method). Image pre-processing techniques including background filling, resizing, noise reduction, and highlighting are exercised at the pre-processing stage. The segmentation process provides a new CNN architecture with five convolution layers that does have a low computational cost. Compared to other publications using the same data set, the proposed method seems to have a success rate of 99.48 percent in the detection of retinal disorders, closing a significant gap in the literature. The proposed approach has the advantage of maintaining low computing costs in comparison to other studies in the literature. When the conclusions are regarded, it is noticed that the suggested method might be exerted as a decision support system to assist physicians in the clinical context during the diagnosis of retinal disorders.

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Retinal Disease Classification from Retinal-OCT Images Using Deep Learning Methods

Naik

Pavana

Sooda³

2022

Machine Intelligence and Data Science Applications

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Classification of Retinal Diseases from OCT scans using Convolutional Neural Networks

Cited by 15 publications

References 5 publications

Transfer learning‐based platform for detecting multi‐classification retinal disorders using optical coherence tomography images

Transfer learning‐based platform for detecting multi‐classification retinal disorders using optical coherence tomography images

Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

Retinal Disease Classification from Retinal-OCT Images Using Deep Learning Methods

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