Retinal Disease Classification Research Articles

OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images

Background and ObjectiveRetinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina. MethodsIn the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm. ResultsTraining and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases. ConclusionIn the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina’s actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.

Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.

Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical application and ignores the internal relation of disease and lesion features. In this paper, a framework of cascaded convolutional networks is proposed to jointly classify retinal diseases and segment lesions. First, we adopt an auxiliary binary classification network to identify normal and abnormal images. Then a novel, to the best of our knowledge, U-shaped multi-task network, BDA-Net, combined with a bidirectional decoder and self-attention mechanism, is used to further analyze abnormal images. Experimental results show that the proposed method reaches an accuracy of 0.9913 in classification and achieves an improvement of around 3% in Dice compared to the baseline U-shaped model in segmentation.

Deep learning-based automated detection of retinal diseases using optical coherence tomography images.

Retinal disease classification is a significant problem in computer-aided diagnosis (CAD) for medical applications. This paper is focused on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The proposed classification algorithm adopted an ensemble of four classification model instances to identify retinal OCT images, each of which was based on an improved residual neural network (ResNet50). The experiment followed a patient-level 10-fold cross-validation process, on development retinal OCT image dataset. The proposed approach achieved 0.973 (95% confidence interval [CI], 0.971-0.975) classification accuracy, 0.963 (95% CI, 0.960-0.966) sensitivity, and 0.985 (95% CI, 0.983-0.987) specificity at the B-scan level, achieving a matching or exceeding performance to that of ophthalmologists with significant clinical experience. Other performance measures used in the study were the area under receiver operating characteristic curve (AUC) and kappa value. The observations of the study implied that multi-ResNet50 ensembling was a useful technique when the availability of medical images was limited. In addition, we performed qualitative evaluation of model predictions, and occlusion testing to understand the decision-making process of our model. The paper provided an analytical discussion on misclassification and pathology regions identified by the occlusion testing also. Finally, we explored the effect of the integration of retinal OCT images and medical history data from patients on model performance.

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

AbstractIn this article, an attempt is made to combine both transform and spatial domain features to increase the accuracy, sensitivity, and specificity for the classification of retinal diseases. Papilloedema, macular edema, glaucoma, diabetic retinopathy (DR), and central retinal vein occlusion (CRVO) are the leading cause of blindness in humans. To identify these diseases in a mass screening process, it consumes intense labor and time. In this proposed work, an automated computer aided algorithm is developed to classify the normal from the abnormal (Papilledema, macular edema, glaucoma, DR, and CRVO) images. For any classification task, an increase in data size, number of classes, and dimension of the feature space affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem. In the proposed algorithm, the feature fusion technique is employed to reduce the input data to the classifier. The features are extracted from wavelet packet transform (WPT) and intensity hue saturation (IHS). A support vector machine (SVM)‐based learning algorithm is used to train the classifier using the extracted features. The proposed method yields better results than the existing classifier fusion method with an overall accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and area under ROC curve (AUC) of 96.3%, 95.8%, 97.8%, 96.4%,98.1%, and 0.971 respectively.

The application value of deep learning OCT on wet age-related macular degeneration assisted diagnosis

Objective To investigate the application value of deep learning optical coherence tomography (OCT) on wet age-related macular degeneration (wAMD) assisted diagnosis. Methods Weakly supervised deep learning algorithms was applied on the premise that only disease or not can be provided as a marker.The OCT image automatically assisted in the diagnosis of diseased areas of wAMD, and thermograms were applied to provide a basis for doctors to detect disease areas.Based on the deep learning of weak supervision, a new network algorithm structure was proposed for detecting disease area in ophthalmic OCT images.At the same time, thermograms were adopted to improve the accuracy of the lesion map, which is the location of the lesion area.This study followed the Declaration of Helsinki.This study protocol was approved by Ethic Committee of Ningbo Eye Hospital (No.2018-YJ05). Written informed consent was obtained from each subject before entering study cohort. Results Resnet-based deep learning algorithm gave a diagnostic accuracy rate of 94.9% for the disease, which was much higher than that of AlexNet 85.3%, VGG 88.7%, and Google-Net 89.2%.The thermograms with different colors provided a more convenient auxiliary diagnosis basis for doctors. Conclusions Compared with the original classification network, which needs disease area markers as empirical knowledge, deep learning algorithm model not only provides better results in the classification of retinal diseases, but also marks potential disease areas.The lesion area provides a basis for judging the area of the lesion for the diagnosis of wAMD. Key words: Wet age-related macular degeneration; Disease classification; Lesion area detection; Weak supervision deep learning

Integrating Handcrafted and Deep Features for Optical Coherence Tomography Based Retinal Disease Classification

Deep neural networks (DNNs) have been widely applied to the automatic analysis of medical images for disease diagnosis and to help human experts by efficiently processing immense amounts of images. While the handcrafted feature has been used for eye disease detection or classification since the 1990s, DNN was recently adopted in this area and showed a very promising performance. Since handcrafted and deep feature can extract complementary information, we propose, in this paper, three different integration frameworks to combine handcrafted and deep feature for optical coherence tomography image-based eye disease classification. In addition, to integrate the handcrafted feature at the input and fully connected layers using existing networks, such as VGG, DenseNet, and Xception, a novel ribcage network (RC Net) is also proposed for feature integration at middle layers. For RC Net, two “rib” channels are designed to independently process deep and handcrafted features, and another so-called “spine” channel is designed for the integration. While dense blocks are the main components of the three channels, sum operation is proposed for the feature map integration. Our experimental results showed that the deep networks achieved better classification accuracy after the integration of the handcrafted features, e.g., scale-invariant feature transform and Gabor. The RC Net showed the best performance among all the proposed feature integration methods.

Automated Classification of Retinal Diseases in STARE Database

Automated Classification of Retinal Diseases in STARE Database

Classification of retinal diseases based on OCT Images.

Optical Coherence Topography (OCT) is an emerging biomedical imaging technology that offers non-invasive real-time, high-resolution imaging of highly scattering tissues. It is widely used in ophthalmology to perform diagnostic imaging on the structure of the anterior eye and the retina. Clinical studies are carried out to assess the application of OCT for some retinal diseases. OCT can provide means for early detection for various types of diseases because morphological changes often occur before the physical symptoms of these diseases. In addition, follow-up imaging can assess treatment effectiveness and recurrence of a disease. A review in this area is needed to identify the results and the findings from OCT images in the field of retinal diseases and how to use these findings to help in clinical applications. This paper overviews the current techniques that are developed to determine the ability of OCT images for early detection/diagnosis of retinal diseases. Also, the paper remarks several challenges that face the researchers in the analysis of the OCT retinal images.

Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images Using Texture Analysis of Macula

Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images Using Texture Analysis of Macula