Multi-scale convolutional neural network for automated AMD classification using retinal OCT images

Sotoudeh-Paima, Saman; Jodeiri, Ata; Hajizadeh, Fedra; Soltanian-Zadeh, Hamid

doi:10.1016/j.compbiomed.2022.105368

Cited by 60 publications

(21 citation statements)

References 44 publications

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“…Saman et al [32] introduced a multi-scale convolutional neural network (CNN) approach for the classification of age-related macular degeneration (AMD) retinal pathologies. The researchers employed the VGG 16 model in conjunction with the Feature Pyramid Network technique, resulting in a commendable accuracy rate of 93.4%.The increased number of trainable parameters results in a longer duration for training.…”

Section: Discussionmentioning

confidence: 99%

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Prabha,

Venkatesan,

Fathimal

et al. 2024

Biomed. Phys. Eng. Express

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Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

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Section: Discussionmentioning

confidence: 99%

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Prabha,

Venkatesan,

Fathimal

et al. 2024

Biomed. Phys. Eng. Express

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“…Karthik et al 23 have proposed an activation function that helps in improving the contrast of the feature maps and they have also suggested architectural changes in the residual connection of the ResNet architecture. Paima et al 13 have suggested the use multi‐scale feature pyramid network for the diagnosis of normal versus AMD classes of OCT images. They have achieved 93.4% classification accuracy on the unseen test set of the UCSD dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Optical coherence tomography (OCT) has recently been used to diagnose different types of retinal diseases 11 . It is primarily used in the diagnosis of retinal disorders, such as CNV, drusen and DME 12,13 . The OCT‐based imaging technique is popular since it provides detailed cross‐sectional images of the retina and macula.…”

Section: Introductionmentioning

confidence: 99%

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MacD‐Net: An automatic guided‐ensemble approach for macular pathology detection using optical coherence tomography images

Maurya,

Pandey,

Joshi

et al. 2023

Int J Imaging Syst Tech

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In this work, a novel guided‐ensemble approach has been proposed for an automated diagnosis of three different types of retinal disorders, such as drusen, choroidal neovascularization (CNV) and diabetic macular edema (DME). These conditions, if left untreated, can lead to vision loss. In the proposed approach, four different pre‐trained CNNs such as MobileNetV1, EfficientNetB3, NASNetMobile and XceptionNet have been fine‐tuned for that purpose and the prediction score obtained by each CNN in an ensemble has been combined based on their validation accuracies. The proposed guided‐ensemble approach has been trained and tested on the public dataset consisting of 84 495 retinal OCT images for training and 1000 unseen images for testing. These images were graded by the experts into four categories, such as CNV, DME, drusen and normal. The proposed approach has outperformed the other methods by obtaining an accuracy of 99.8% in classifying four different classes of retinal OCT images.

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“…However, development of artificial intelligence (AI)-based medical image analysis methods can help overcome the abovementioned challenges. As one of the subsets of AI, deep learning algorithms have been found to be effective in different medical fields, such as radiology, dermatology, ophthalmology, and pathology (9,10).…”

Section: Introductionmentioning

confidence: 99%

A Multi-centric Evaluation of Deep Learning Models for Segmentation of COVID-19 Lung Lesions on Chest CT Scans

et al. 2022

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Background: Chest computed tomography (CT) scan is one of the most common tools used for the diagnosis of patients with coronavirus disease 2019 (COVID-19). While segmentation of COVID-19 lung lesions by radiologists can be time-consuming, the application of advanced deep learning techniques for automated segmentation can be a promising step toward the management of this infection and similar diseases in the future. Objectives: This study aimed to evaluate the performance and generalizability of deep learning-based models for the automated segmentation of COVID-19 lung lesions. Patients and Methods: Four datasets (2 private and 2 public) were used in this study. The first and second private datasets included 297 (147 healthy and 150 COVID-19 cases) and 82 COVID-19 subjects. The public datasets included the COVID19-P20 (20 COVID-19 cases from 2 centers) and the MosMedData datasets (50 COVID-19 patients from a single center). Model comparisons were made based on the Dice similarity coefficient (DSC), receiver operating characteristic (ROC) curve, and area under the curve (AUC). The predicted CT severity scores by the model were compared with those of radiologists by measuring the Pearson’s correlation coefficients (PCC). Also, DSC was used to compare the inter-rater agreement of the model and expert against that of 2 experts on an unseen dataset. Finally, the generalizability of the model was evaluated, and a simple calibration strategy was proposed. Results: The VGG16-UNet model showed the best performance across both private datasets, with a DSC of 84.23% ± 1.73% on the first private dataset and 56.61% ± 1.48% on the second private dataset. Similar results were obtained on public datasets, with a DSC of 60.10% ± 2.34% on the COVID19-P20 dataset and 66.28% ± 2.80% on a combined dataset of COVID19-P20 and MosMedData. The predicted CT severity scores of the model were compared against those of radiologists and were found to be 0.89 and 0.85 on the first private dataset and 0.77 and 0.74 on the second private dataset for the right and left lungs, respectively. Moreover, the model trained on the first private dataset was examined on the second private dataset and compared against the radiologist, which revealed a performance gap of 5.74% based on DSCs. A calibration strategy was employed to reduce this gap to 0.53%. Conclusion: The results demonstrated the potential of the proposed model in localizing COVID-19 lesions on CT scans across multiple datasets; its accuracy competed with the radiologists and could assist them in diagnostic and treatment procedures. The effect of model calibration on the performance of an unseen dataset was also reported, increasing the DSC by more than 5%.

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Multi-scale convolutional neural network for automated AMD classification using retinal OCT images

Cited by 60 publications

References 44 publications

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

MacD‐Net: An automatic guided‐ensemble approach for macular pathology detection using optical coherence tomography images

A Multi-centric Evaluation of Deep Learning Models for Segmentation of COVID-19 Lung Lesions on Chest CT Scans

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