A Novel Hybrid Optic Disc Detection and Fovea Localization Method Integrating Region-Based Convnet and Mathematical Approach

Dinç, Barış; Kaya, Yasin

doi:10.1007/s11277-023-10255-0

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…Kaya (2020) employed adaptive histogram equalization to ensure consistent brightness and contrast values across all images, thereby improving the reliability of optic disc (OD) localization. Dinç and Kaya (2023) used Gaussian blur as a preprocessing step before fovea localization to reduce the impact of noise related to pixel intensity caused by tissue damage on OD localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

Multi-scale multi-attention network for diabetic retinopathy grading

Xia,

Long,

Song

et al. 2023

Phys. Med. Biol.

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Objective. Diabetic retinopathy (DR) grading plays an important role in clinical diagnosis. However, automatic grading of DR is challenging due to the presence of intra-class variation and small lesions. On the one hand, deep features learned by convolutional neural networks often lose valid information about these small lesions. On the other hand, the great variability of lesion features, including differences in type and quantity, can exhibit considerable divergence even among fundus images of the same grade. To address these issues, we propose a novel multi-scale multi-attention network (MMNet). Approach. Firstly, to focus on different lesion features of fundus images, we propose a lesion attention module, which aims to encode multiple different lesion attention feature maps by combining channel attention and spatial attention, thus extracting global feature information and preserving diverse lesion features. Secondly, we propose a multi-scale feature fusion module to learn more feature information for small lesion regions, which combines complementary relationships between different convolutional layers to capture more detailed feature information. Furthermore, we introduce a Cross-layer Consistency Constraint Loss to overcome semantic differences between multi-scale features. Main results. The proposed MMNet obtains a high accuracy of 86.4% and a high kappa score of 88.4% for multi-class DR grading tasks on the EyePACS dataset, while 98.6% AUC, 95.3% accuracy, 92.7% recall, 95.0% precision, and 93.3% F1-score for referral and non-referral classification on the Messidor-1 dataset. Extensive experiments on two challenging benchmarks demonstrate that our MMNet achieves significant improvements and outperforms other state-of-the-art DR grading methods. Significance. MMNet has improved the diagnostic efficiency and accuracy of diabetes retinopathy and promoted the application of computer-aided medical diagnosis in DR screening.

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Section: Related Workmentioning

confidence: 99%

Multi-scale multi-attention network for diabetic retinopathy grading

Xia,

Long,

Song

et al. 2023

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning has significantly improved the interpretation of fundus images, including tasks like vessel segmentation [ 16 , 17 ], optic disc and fovea (the center of the macula) localization [ 18 , 19 ], and image registration [ 20 , 21 ]. For vessel segmentation, researchers in [ 16 ] noticed that existing UNet-based models [ 22 ] can lose information due to multiple pooling steps and insufficient processing of local context features.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, an attention fusion block was created to better capture features at different scales. For optic disc and fovea localization, a two-stage method in [ 18 ] integrates deep learning with a mathematical model. They first locate the optic disc using Faster RCNN [ 23 ], and then use its position to find the fovea within a ring-shaped area.…”

Section: Related Workmentioning

confidence: 99%

Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images

Pham,

Le,

Bum

et al. 2023

Bioengineering

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Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes.

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Human activity recognition from multiple sensors data using deep CNNs

Kaya

Topuz

2023

Multimed Tools Appl

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A Novel Hybrid Optic Disc Detection and Fovea Localization Method Integrating Region-Based Convnet and Mathematical Approach

Cited by 11 publications

References 40 publications

Multi-scale multi-attention network for diabetic retinopathy grading

Multi-scale multi-attention network for diabetic retinopathy grading

Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images

Human activity recognition from multiple sensors data using deep CNNs

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