Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

Pavithra, Kc; Kumar, Preetham; Geetha, M. Kalaiselvi; Bhandary, Sulatha V.

doi:10.1016/j.bbe.2022.12.005

Cited by 11 publications

(4 citation statements)

References 217 publications

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“…Optical Coherence Tomography (OCT) is a novel and effective screening tool for ophthalmic examinatio. OCT images have the characteristics of high resolution, high contrast and non-intrusion [14]. The challenges of OCT image classification mainly include category similarity, data noise, large amount of data and high dimension, small amount of annotated data and benchmark standard controversy, etc [14].…”

Section: Related Work a Retinal Disease Oct Images Classificationmentioning

confidence: 99%

“…OCT images have the characteristics of high resolution, high contrast and non-intrusion [14]. The challenges of OCT image classification mainly include category similarity, data noise, large amount of data and high dimension, small amount of annotated data and benchmark standard controversy, etc [14]. Advanced algorithms and technologies are needed to overcome these challenges.…”

Section: Related Work a Retinal Disease Oct Images Classificationmentioning

confidence: 99%

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A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

Zhang,

Qian,

et al. 2024

IEEE Access

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Retinal disease detection and diagnosis relying solely on artificial retinal diseases will bring great pressure to doctors and increase the rate of misdiagnosis. Therefore, the development of computer vision technology has brought the possibility for ophthalmologists to use computer-aided diagnosis. In recent years, most models for retinal disease recognition have been based on deep learning, which has the disadvantage of requiring large amounts of data and training time. It is also partly based on broad learning and its disadvantages are that feature extraction ability is limited and poor scalability. To overcome these limitations, we propose a novel artificial intelligence-based approach for automatic assessment of retinal disease images called multi-view deep-broad learning network (MDBL-Net), which absorbs the advantages of deep learning and broad learning. MDBL-Net comprises a Multi-view and Multi-scale Feature Extraction (MMFE) module and a Multi-scale Aggregation (MA) block. The MMFE module extracts features of different scales by learning feature representations from multiple views, while the MA block fully aggregates multi-scale deep-broad features from low-level to high-level representations. Experiments were conducted on two public datasets, UCSD and OCT2017. Experiments were conducted on two public datasets, UCSD and OCT2017, and results demonstrate that MDBL-Net achieves high accuracy even with limited training data (only 1%) and significantly reduces training time compared to traditional deep learning models. Specifically, MDBL-Net achieved an accuracy of 96.93% on the UCSD dataset and 99.90% on the OCT2017 dataset, outperforming state-of-the-art models in both cases. These findings suggest that the proposed MDBL-Net approach holds great promise for the task of retinal disease screening and recognition." INDEX TERMS Broad learning, image classification, medical image, retinal disease, computer aided diagnosis.

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Section: Related Work a Retinal Disease Oct Images Classificationmentioning

confidence: 99%

Section: Related Work a Retinal Disease Oct Images Classificationmentioning

confidence: 99%

A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

Zhang,

Qian,

et al. 2024

IEEE Access

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“…Diabetic retinopathy is characterized in diverse stages by the occurrence of various abnormalities in the retinal layer, such as microaneurysms, intra‐retinal hemorrhages, vitreous hemorrhages, exudates, venous beadings, intra‐retinal microvascular abnormalities (IRMA) and neovascularization (Pavithra et al, 2023) as shown in Figure 1. The symptoms are classified into three categories: red lesions, bright lesions, and neovascularization.…”

Section: Introductionmentioning

confidence: 99%

Micro‐segmentation of retinal image lesions in diabetic retinopathy using energy‐based fuzzy C‐Means clustering (EFM‐FCM)

Naz,

Nijhawan,

Ahuja

et al. 2023

Microscopy Res & Technique

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Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy‐based model into the Fuzzy C‐Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M‐FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management.Research Highlights A novel approach is proposed in the article, integrating a neural network energy‐based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M‐FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.

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“…Therefore, a timely and accurate identification of DME is imperative for the effective management of this disease. Several attempts have been made over the years to develop artificial intelligence (AI)-based decision support systems (DSS) for the treatment of ocular disorders by utilizing medical images and discerning the efficacy of deep learning-based models [3][4][5][6][7][8][9][10]. Given the necessity for real-world adaptability, the consensus veers towards a transparent, explainable decision rendered by DSS as more desirable compared to an opaque, albeit highly accurate, decision.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

et al. 2023

Self Cite

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Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

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Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

Cited by 11 publications

References 217 publications

A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

Micro‐segmentation of retinal image lesions in diabetic retinopathy using energy‐based fuzzy C‐Means clustering (EFM‐FCM)

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

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