A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images

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

doi:10.1016/j.bspc.2021.102538

Cited by 43 publications

(18 citation statements)

References 32 publications

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“…Additionally, the proposed method was compared with the method using ResNet-50 alone, as shown in Figure 6 and Figure 7 . The comparison was also made with previous methods, such as those in [ 26 , 31 , 32 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], as shown in Table 4 and Table 6 , based on accuracy and AUC. The results suggest that the proposed method performed very well compared with previous models when classifying AMD and normal OCT images.…”

Section: Resultsmentioning

confidence: 99%

“…Multi-scale and multi-path CNN [31] 99.78 0.9978 Multi-scale CNN [32] 99.73 0.9999 Inception V3 transfer learning [42] 96.53 0.9762 Multi-scale deep feature fusion [44] 97.71 0.9900 AlexNet transfer learning [45] 98.26 0.9917 Iterative fusion CNN [46] 93.40 0.9798 Proposed method 99.87 1.0000…”

Section: Methods Weighted Average Accuracy % Aucmentioning

confidence: 99%

“…Thomas et al [ 31 , 32 ] conducted a number of studies based on AMD detection using OCT images. In [ 31 ], a multiscale and multipath CNN with six convolutional layers was proposed and finally achieved an overall accuracy of 98.79% with the random forest (RF) classifier.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 31 ], a multiscale and multipath CNN with six convolutional layers was proposed and finally achieved an overall accuracy of 98.79% with the random forest (RF) classifier. Later, in [ 32 ], they introduced another novel multiscale CNN with seven convolutional layers to classify AMD and normal OCT images. The multiscale convolution layer enables a large number of local structures to be generated with various filter sizes.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, a computer-aided diagnosis (CAD) system that can automatically detect AMD from a large number of retinal OCT images is urgently needed. In recent years, researchers have launched kinds of studies on AMD detection and classification based on retinal OCT images with the development and improvement of computer technology and image processing algorithms [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Zhou

Zou

2022

Diagnostics

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Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD.

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Section: Resultsmentioning

confidence: 99%

Section: Methods Weighted Average Accuracy % Aucmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Zhou

Zou

2022

Diagnostics

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A deep learning LSTM-based approach for AMD classification using OCT images

Hamid,

Elnokrashy,

Abdelhay

et al. 2024

Neural Comput & Applic

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Age-related macular degeneration (AMD) is an age-related, persistent, painless eye disease that impairs central vision. The central area (macula) of the retina, located at the back of the eye, sustains damage that is the cause of loss of vision. The early detection of AMD can increase the probability of treatment and prevent vision loss. The AMD can be classified into dry and wet AMD based on the absence of neovascularization. This study introduces a new methodology for the classification of AMD using optical coherence tomography (OCT) retinal images. The proposed methodology is based on three stages. The first stage is the data preparation stage for resizing and normalizing the used images. The second stage is the image processing stage for enhancing the image quality as contrast and resolution these enhancements have been checked by the weighted peak signal-to-noise ratio (WPSNR) methodology. The third stage is the deep feature extraction and classification stage, which consists of two sub-models. The first model is MobileNet V1 which has been used as a deep feature extractor. The second model is LSTM (long short-term memory), fed with deep features to classify the AMD stages. A multi-classification with six separate trials has been employed with the proposed methodology, and compared with other models like DenseNet201 and InceptionV3. The proposed model has been tested on a sample of benchmark data with 4005 grayscale images labeled into three classes. The proposed methodology has achieved an accuracy of 98.85%, a sensitivity of 99.09%, and a specificity of 99.1%. To ensure the effectiveness of the proposed methodology, a comparative analysis has been established with previous approaches in the related field, and the results demonstrated the superiority of the proposed system in AMD multi-classification.

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AI-based methods for detecting and classifying age-related macular degeneration: a comprehensive review

El-Den,

Elsharkawy,

Saleh

et al. 2024

Artif Intell Rev

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A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images

Cited by 43 publications

References 32 publications

Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

A deep learning LSTM-based approach for AMD classification using OCT images

AI-based methods for detecting and classifying age-related macular degeneration: a comprehensive review

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