Comparisonal study of Deep Learning approaches on Retinal OCT Image (original) (raw)
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Retinal Disease Classification from OCT Images Using Deep Learning Algorithms
2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021
Optical Coherence Tomography (OCT) is a noninvasive test that takes cross-section pictures of the retina layer of the eye and allows ophthalmologists to diagnose based on the retina's layers. Therefore, it is an important modality for the detection and quantification of retinal diseases and retinal abnormalities. Since OCT provides several images for each patient, it is a time consuming work for ophthalmologists to analyze the images. This paper proposes deep learning models that categorize patients' OCT images into four categories such as Choroidal neovascularization (CNV), Diabetic macular edema (DME), Drusen, and Normal. Two different models are proposed. One is using three binary Convolutional Neural Network (CNN) classifiers and the other is using four binary CNN classifiers. Several CNNs, such as VGG16, VGG19, ResNet50, ResNet152, DenseNet121, and InceptionV3, are adapted as feature extractors to develop the binary classifiers. Among them, the proposed model using VGG16 for CNV vs. Other classes, VGG16 for DME vs. other classes, VGG19 for Drusen vs. Other classes, and InceptionV3 for Normal vs. other classes shows the best performance with 0.987 accuracy, 0.987 sensitivity, and 0.996 specificity. The binary classifier for Normal class has 0.999 accuracy. These results show their potential to work as a second reader for ophthalmologists.
Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images
Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images, 2024
Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three di erent diseases from features extracted from Optical Coherence Tomography (OCT) images. The deeplearning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.
Applying Artificial Intelligence to Detect Retinal Diseases
Proceedings on Engineering Sciences
Vision and eye health are one of the most crucial things in human life, it needs to be preserved to maintain the life of the individuals. Eye diseases such as CNV, DRUSEN, AMD, and DME are mainly caused due to the damages of the retina, and since the retina is damaged and discovered at late stages, there is almost no chance to reverse vision and cure it, which means that the patient will lose the power of vision partially and maybe entirely. Optical Coherence Tomography is an advanced scanning device that can perform non-invasive cross-sectional imaging of internal structures in biological tissues by measuring their optical reflections. This will help the ophthalmologists to take a clear look on the back of the eye and determine at early stages the damage caused to the retina, macula, and optic nerve. The aim of this study is to propose a novel classification model based on deep learning and transfer learning to automatically classify the different retinal diseases using retinal images obtained from Optical Coherence Tomography (OCT) device. We propose a deep CNN architecture and compared the obtained results with pre-trained models such as Inception V3 and VGG-16, our proposed CNN architecture gave an accuracy of 98.96% and Inception V3 model gave accuracy up to 99.27% on the test set.
A Deep Learning-Based Framework for Retinal Disease Classification
Healthcare
This study addresses the problem of the automatic detection of disease states of the retina. In order to solve the abovementioned problem, this study develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. The model (VGG-19 architecture) is empowered by transfer learning. The model is designed so that it can learn from a large set of images taken with optical coherence tomography (OCT) and classify them into four conditions of the retina: (1) choroidal neovascularization, (2) drusen, (3) diabetic macular edema, and (4) normal form. The training datasets (taken from publicly available sources) consist of 84,568 instances of OCT retinal images. The datasets exhibit all four classes of retinal disease mentioned above. The proposed model achieved a 99.17% classification accuracy with 0.995 specificities and 0.99 sensitivity, making it better than the existing models. In addition, the proper sta...
Application of deep learning for retinal image analysis: A review
Computer Science Review, 2020
Retinal image analysis holds an imperative position for the identification and classification of retinal diseases such as Diabetic Retinopathy (DR), Age Related Macular Degeneration (AMD), Macular Bunker, Retinoblastoma, Retinal Detachment, and Retinitis Pigmentosa. Automated identification of retinal diseases is a big step towards early diagnosis and prevention of exacerbation of the disease. A number of state-of-the-art methods have been developed in the past that helped in the automatic segmentation and identification of retinal landmarks and pathologies. However, the current unprecedented advancements in deep learning and modern imaging modalities in ophthalmology have opened a whole new arena for researchers. This paper is a review of deep learning techniques applied to 2-D fundus and 3-D Optical Coherence Tomography (OCT) retinal images for automated classification of retinal landmarks, pathology, and disease classification. The methodologies are analyzed in terms of sensitivity, specificity, Area under ROC curve, accuracy, and F score on publicly available datasets which includes DRIVE, STARE, CHASE_DB1, DRiDB, NIH AREDS, ARIA, MESSIDOR-2, E-OPTHA, EyePACS-1 DIARETDB and OCT image datasets.
Acta Scientiarum. Technology
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, no...
An Approach to Detect Eye Diseases Using Deep Learning
Zenodo (CERN European Organization for Nuclear Research), 2023
Eye disease can cause vision impairment or blindness and early detection is crucial for effective treatment. Machine learning techniques have shown potential in detecting eye diseases by analysing digital images of the eye. These techniques can accurately classify three types of eye diseases, including Cataract, Diabetic retinopathy, and Redness. These models can be trained on large datasets of eye images and can also be used for screening in remote areas where access to eye specialists is limited. Despite the promising results, there are still some challenges in the application of machine learning for eye disease detection, such as the need for large annotated datasets and the requirement for high-quality images. Nonetheless, the integration of machine learning with traditional ophthalmologic methods can lead to improved accuracy and speed in disease detection and ultimately improve patient outcomes.
Using deep learning to diagnose retinal diseases through medical image analysis
International Journal of Electrical and Computer Engineering (IJECE), 2024
The scientific article focuses on the application of deep learning through simple U-Net, attention U-Net, residual U-Net, and residual attention U-Net models for diagnosing retinal diseases based on medical image analysis. The work includes a thorough analysis of each model's ability to detect retinal pathologies, taking into account their unique characteristics such as attention mechanisms and residual connections. The obtained experimental results confirm the high accuracy and reliability of the proposed models, emphasizing their potential as effective tools for automated diagnosis of retinal diseases based on medical images. This approach opens up new prospects for improving diagnostic procedures and increasing the efficiency of medical practice. The authors of the article propose an innovative method that can significantly facilitate the process of identifying retinal diseases, which is critical for early diagnosis and timely treatment. The results of the study support the prospect of using these models in clinical practice, highlighting their ability to accurately analyze medical images and improve the quality of eye health care.
Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning
International Journal of Biomedical Imaging
Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.
Computer Methods and Programs in Biomedicine, 2019
Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal diseases. Methods: This article presents a new deep learning model, OCT-NET, which is a customized convolutional neural network for processing scans extracted from optical coherence tomography volumes. OCT-NET is applied to the classification of three conditions seen in SD-OCT volumes. Additionally, the proposed model includes a feedback stage that highlights the areas of the scans to support the interpretation of the results. This information is potentially useful for a medical specialist while assessing the prediction produced by the model. Results: The proposed model was tested on the public SERI-CUHK and A2A SD-OCT data sets containing healthy, diabetic retinopathy, diabetic macular edema and age-related macular degeneration. The experimental evaluation shows that the proposed method outperforms conventional convolutional deep learning models from the state of the art reported on the SERI+CUHK and A2A SD-OCT data sets with a precision of 93% and an area under the ROC curve (AUC) of 0.99 respectively. Conclusions: The proposed method is able to classify the three studied retinal diseases with high accuracy. One advantage of the method is its ability to produce interpretable clinical information in the form of highlighting the regions of the image that most contribute to the classifier decision.