Eye Disease Prediction from Optical Coherence Tomography Images with Transfer Learning

Bhowmik, Arka; Kumar, Sanjay; Bhat, Neeraj

doi:10.1007/978-3-030-20257-6_9

Cited by 32 publications

(10 citation statements)

References 14 publications

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“…However, it is remarked that pre-trained CNN architectures cannot provide the expected high performance in the classification of retinal diseases, and they show lower performances or close performances to each other than the CNN architectures designed in terms of diagnosing the retinal diseases. Among the studies compared, there are methods whose calculation cost is close to or lower than the proposed method (Alqudah, 2020;Apon et al, 2021;Berrimi & Moussaoui, 2020;Bhowmik et al, 2019;Najeeb et al, 2019;Tayal et al, 2021). However, surveying the results attained, it may be spotted that the performance loss is high.…”

Section: Resultsmentioning

confidence: 99%

“…It also managed to detect hyperreflective foci (hRF) within druseniod lesions with a sensitivity of 78% and a specificity of 100%, while it detected the hyporeflective foci (hRF) within druseniod lesions with 79% precision and 95% specificity. Researchers in general reported relatively successful results in different transfer learning architectures (Bhowmik, Kumar, & Bhat, 2019;Kamble et al, 2018;Wang et al, 2019). Wang et al (Wang et al, 2020) combined two CNN architectures and proposed a new method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because one of the disadvantages of deep learning models is that they need a lot of data for training. For this reason, architectures such as ImageNet, which are trained with big data before and achieve very successful results, are mostly used in the studies (Berrimi & Moussaoui, 2020;Bhowmik et al, 2019;Kermany et al 2018;Ye et al, 2019). These pre-trained models have learned how to analyze the image, and it is possible to retrain and exploit the model with less data for our problems by using the feature maps of these models.…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

2022

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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, noise reduction, and highlighting are exercised at the pre-processing stage. The segmentation process provides a new CNN architecture with five convolution layers that does have a low computational cost. Compared to other publications using the same data set, the proposed method seems to have a success rate of 99.48 percent in the detection of retinal disorders, closing a significant gap in the literature. The proposed approach has the advantage of maintaining low computing costs in comparison to other studies in the literature. When the conclusions are regarded, it is noticed that the suggested method might be exerted as a decision support system to assist physicians in the clinical context during the diagnosis of retinal disorders.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

2022

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“…This approach resulted in an accuracy of 87.5%, 93.5% sensitivity, and 81% specificity. Bhowmik et al 18 employed both the Inception V3 and VGG16 models as baseline networks and used transfer learning to analyze images and predict retinal diseases. This led to an accuracy of 94%.…”

Section: Introductionmentioning

confidence: 99%

Optimizing deep learning based retinal diseases classification on optical coherence tomography scans

Mandal,

Phatak

2023

Optical Coherence Imaging Techniques and Imaging in Scattering Media V

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Optical Coherence Tomography (OCT) is a widely used medical imaging technique for diagnosing various eye conditions and for assessing the health of the retina and other structures in the eye. As the amount of data generated from OCT scans increases day by day, the need for automated support systems for medical staff becomes more pressing. In this study, we used a computer based diagnostic system for detecting and classifying three retinal diseases (Choroidal Neovascularization (CNV), Drusen, Diabetic Macular Edema (DME)) in OCT Bscans. Our system applies Convolutional Neural Networks (EfficeintNet) and Transfer learning to make accurate predictions of the diseases or healthy eye. Transfer learning, a technique commonly used in deep learning, is implemented to train EfficientNet architectures using a publicly available dataset (OCT2017) of approx 84000 OCT B-scans. EfficientNet models were chosen for their known efficiency in terms of performance and their ability to classify effectively with fewer parameters and lower computational requirements in comparison to other Convolutional Neural Networks (CNN) models. We used EfficeintNet models that were pretrained on ImageNet dataset and fine-tuned the models on our dataset. In order to assess the effectiveness of transfer learning and fine-tuning, we evaluated their performance on an imbalanced multiclass classification task using metrics like F1 Score, Precision, Recall, Accuracy, and Confusion Matrices (CM). The results of our evaluations are presented in the form of CM for each class and model.We tried out various EfficientNet models (B0, B3, B5) as base models utilizing similar data resolution, volume and other hyperparameters, and discovered that larger EfficientNet models did not necessarily lead to better classification performance. We accounted for class imbalance in the data to make our method robust for realworld scenarios. The best result was found to be from lowest complexity model, EfficientNet B0. Our research found that the EfficientNet B0 model demonstrated exceptional performance with a macro average F1 Score of 99.8% and an Accuracy of 99.8%. Additionally, our results also revealed that the EfficientNets B0, B3, B5 models are particularly well-suited for multiclass classification based on highly imbalanced OCT2017 dataset. High classification scores are achieved due to several factors, such as data enhancement, resolution scaling, finetuning, and successful transfer learning using ImageNet weights. Based on our preliminary results our approach performs as well or outperforms other known approaches. Our goal is to provide assistance to medical staff in diagnositic process with the help of artificial intelligence (AI) algorithms. Improving the efficiency and accuracy of diagnosis is important in the field of medicine, and the use of AI algorithms such as the one proposed in this research has the potential to make a significant impact in this regard.

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“…Deep convolutional neural networks (DCNNs) can be used for end to end process of sourcey medical images to produce an expected outcome prediction [8].Visual Geometrical Group (VGG) is a deep neural network with a multi-layered operation and it is based on CNN Model [10].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Based MA Detection TL-MAD

Devi,

Rani

2020

IJEAT

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Diabetic Retinopathy (DR) is a microvascular complication of Diabetes that can lead to blindness if it is severe. Microaneurysm (MA) is the initial and main symptom of DR. In this paper, an automatic detection of DR from retinal fundus images of publicly available dataset has been proposed using transfer learning with pre-trained model VGG16 based on Convolutional Neural Network (CNN). Our method achieves improvement in accuracy for MA detection using retinal fundus images in prediction of Diabetic Retinopathy.

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Eye Disease Prediction from Optical Coherence Tomography Images with Transfer Learning

Cited by 32 publications

References 14 publications

Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

Optimizing deep learning based retinal diseases classification on optical coherence tomography scans

Transfer Learning Based MA Detection TL-MAD

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