Automated Detection of Age-Related Macular Degeneration from OCT Images Using Multipath CNN

Thomas, Anju; Harikrishnan, Pandurangan; Krishna, Adithya K.; Palanisamy, P.; Gopi, Varun P.

doi:10.5626/jcse.2021.15.1.34

Cited by 15 publications

(3 citation statements)

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“…Most of the previous works in this field are based on detecting one or two diseases from OCT images [26], [27], [28], [26]. As we explore detecting several diseases in two different datasets, there are few works whose research criteria are similar to ours.…”

Section: Comparison With Other Workmentioning

confidence: 95%

ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images

Rahimzadeh,

Mohammadi

2022

Preprint

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Optical coherence tomography (OCT) imaging is a well-known technology for visualizing retinal layers and helps ophthalmologists to detect possible diseases. Accurate and early diagnosis of common retinal diseases can prevent the patients from suffering critical damages to their vision. Computer-aided diagnosis (CAD) systems can significantly assist ophthalmologists in improving their examinations. This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the learning architectures of two robust convolutional models. Spatial resolution is a critical factor in medical images, especially the OCT images that contain tiny essential points. To empower our model, we apply a new post-architecture model to our ensemble model for enhancing spatial resolution learning without increasing computational costs. The introduced post-architecture model can be deployed to any feature extraction model to improve the utilization of the feature map's spatial values. We have collected two open-source datasets for our experiments to make our models capable of detecting six crucial retinal diseases: Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), Diabetic Retinopathy (DR), Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen alongside the normal cases. Our experiments on two datasets and comparing our model with some other well-known deep convolutional neural networks have proven that our architecture can increase the classification accuracy up to 5%. We hope that our proposed methods create the next step of CAD systems development and help future researches. The code of this paper is shared at https://github.com/mr7495/OCT-classification.

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Section: Comparison With Other Workmentioning

confidence: 95%

ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images

Rahimzadeh,

Mohammadi

2022

Preprint

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“…Thomas et al [21] proposed an accurate AMD diagnosis using a new multipath CNN model. The regular and AMD images are classified using multipath CNN with five convolutional layers in the proposed model.…”

Section: Literature Review Review Various Existing DL Methods For Dia...mentioning

confidence: 99%

Automated classification of age-related macular degeneration from optical coherence tomography images using deep learning approach

Nagamani,

Sudhakar

2023

IJ-AI

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<p>Early detection of macular diseases can prevent vision loss. Manual screening can be unreliable due to the similarity in the pathological presentations of common retinal illnesses like age-related macular degeneration (AMD). Researchers are becoming more interested in the accurate automated computer-based detection of macular diseases. Using healthy optical coherence tomography (OCT) images, the drusens (early stage) and choroidal neovascularization (CNV) (late stage) of AMD are thus classified using a completely different approach in this paper. The new deep learning (DL) model is proposed for multiple OCT image segmentation of ophthalmological diseases using attention-based nested U-Net (ANU-Net). The flower pollination optimization algorithm (FPOA) is used to optimize the hyperparameters of the network. The SqueezeNet-based classification can be made in the pre-processed images. A dataset from the University of California San Diego (UCSD) is used to evaluate the proposed method. 98.7% accuracy, 99.8% specificity, and 99.7% sensitivity are achieved by the proposed method. The proposed method produces better identification results for automated preliminary diagnosis of macular diseases in hospitals and eye clinics due to the positive classification results.</p>

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“…[ 7 8 9 10 11 12 13 14 15 16 17 ] While most macular degeneration research has focused on identifying age-related macular degeneration, there have been few studies on myopic maculopathy. [ 18 19 20 21 22 23 24 25 26 ] Among these few studies, Xu et al . [ 27 ] proposed a feature classification method for detecting TF, representing the severity of TF in color fundus photographs by extracting color and texture features.…”

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confidence: 99%

Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet

Zheng,

Zhang,

Zhu

et al. 2023

Indian Journal of Ophthalmology

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Purpose: We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees. Methods: The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models. The diagnostic results were compared with those of the VGG16 and ResNet50 classification models. The leading evaluation indicators were sensitivity, specificity, F1 score, area under the receiver operating characteristic (ROC) curve area under curve (AUC), 95% confidence interval, kappa value, and accuracy. The ROC curves of the ten grading models were also compared. Results: We used 1199 color fundus photographs to evaluate the myopic maculopathy grading models. The size of the EfficientNet-B0 myopic maculopathy grading model was 15.6 MB, and it had the highest kappa value (88.32%) and accuracy (83.58%). The model's sensitivities to diagnose tessellated fundus (TF), diffuse chorioretinal atrophy (DCA), patchy chorioretinal atrophy (PCA), and macular atrophy (MA) were 96.86%, 75.98%, 64.67%, and 88.75%, respectively. The specificity was above 93%, and the AUCs were 0.992, 0.960, 0.964, and 0.989, respectively. Conclusion: The EfficientNet models were used to design grading diagnostic models for myopic maculopathy. Based on the collected fundus images, the models could diagnose a healthy fundus and four types of myopic maculopathy. The models might help ophthalmologists to make preliminary diagnoses of different degrees of myopic maculopathy.

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Automated Detection of Age-Related Macular Degeneration from OCT Images Using Multipath CNN

Cited by 15 publications

References 0 publications

ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images

ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images

Automated classification of age-related macular degeneration from optical coherence tomography images using deep learning approach

Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet

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