Computer-Aided Diagnosis of Ophthalmic Diseases Using OCT Based on Deep Learning: A Review

Zhang, Ruru; He, Jin; Shi, Shenda; Kang, Xiaoyang; Chai, Wei Koong; Li, Meng; Liu, Yu; Haihong, E; Ou, Zhonghong; Song, Meina

doi:10.1007/978-3-030-37429-7_63

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…Processes in Deep Learning for OCT Diagnostics The application of deep learning of eye diseases using OCT follows a series of steps similar to those in other imaging domains. These include initial OCT image preprocessing, segmentation of OCT images, and finally, classification of these images [14], as illustrated in Fig. 1.…”

Section: B Advancements In DL For Computer-assisted Oct In Diagnosing...mentioning

confidence: 99%

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OCT Images Diagnosis Based on Deep Learning – A Review

Abdi,

Abdulazeez

2024

ijcs

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The recent advancements in deep learning technology have significantly transformed the field of medical imaging, namely in the diagnosis of ocular illnesses. The progress made in this field has improved the capacity to extract and evaluate intricate characteristics in images, with Optical Coherence Tomography (OCT) playing a crucial role. OCT has become known for its safe qualities and its high level of detail, rendering it an essential instrument in the diagnosis of eye diseases. The interesting improvement in research is centered around the integration of deep learning with OCT for the purpose of automating the detection of eye diseases. We conducted a comprehensive study that explores several diagnostic methods and the wide-ranging uses of OCT. Additionally, it addresses the accessibility of publicly available datasets that are specifically tailored to optical coherence tomography (OCT). The paper provides a detailed review of the most recent advancements in computer-assisted diagnostic methods for diseases of the eye, such as age-related macular degeneration, glaucoma, and diabetic macular edema, with a particular focus on the effective use of OCT. Moreover, the paper systematically analyzes the primary challenges that deep learning faces in OCT image interpretation, emphasizing the intricate nature and possibilities of this field.

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Section: B Advancements In DL For Computer-assisted Oct In Diagnosing...mentioning

confidence: 99%

“…Figure1. Deep Learning-Based Standard Methods for Ophthalmic Diagnosis with OCT [14] OCT Preprocessing OCT Segmentation…”

Section: B Advancements In DL For Computer-assisted Oct In Diagnosing...mentioning

confidence: 99%

OCT Images Diagnosis Based on Deep Learning – A Review

Abdi,

Abdulazeez

2024

ijcs

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“…In the literature, there are CAD systems that use OCT in the diagnosis and classification of retinal diseases and glaucoma diseases and assist in the decision-making process of the doctor [1][2][3]. Zhang et al [1] analyzed multiple ophthalmic diseases such as glaucoma, AMD, DMA using a CAD system. Rasti et al [2] proposed a multi-scale convolutional mixture of expert (MCME) based CAD system to identify normal retina.…”

Section: Introductionmentioning

confidence: 99%

Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images

Tuncer¹,

Çınar²,

Fırat³

2021

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In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In this paper, a hybrid Convolutional Neural Networks-based CAD system, which can classify Diabetic Macular Edema (DME), Drusen Choroidal Neovascularization (CNV), and normal OCT images, is proposed. The proposed system is CNN-SVM (Convolutional Neural Networks – Support Vector Machine) model and doesn’t require any additional extraction of feature or noise filtering on OCT images. A total of 968 OCT images is classified in pre-trained CNN methods with Alexnet, Resnet18 and Googlenet. Accuracy is achieved with highest Googlenet 97.4%. To examine the performance of the proposed CAD system, the CNN-SVM method achieves 98.96% with the highest accuracy hybrid Alexnet-SVM model, which is implemented with Alexnet-SVM, Resnet18-SVM and Googlenet-SVM models.

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Computer-Aided Diagnosis of Ophthalmic Diseases Using OCT Based on Deep Learning: A Review

Cited by 2 publications

References 29 publications

OCT Images Diagnosis Based on Deep Learning – A Review

OCT Images Diagnosis Based on Deep Learning – A Review

Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images

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