Automatic Glaucoma Detection from Fundus Images Using Deep Convolutional Neural Networks and Exploring Networks Behaviour Using Visualization Techniques

Velpula, Vijaya Kumar; Sharma, Lakhan Dev

doi:10.1007/s42979-023-01945-4

Cited by 7 publications

(6 citation statements)

References 46 publications

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“…In the Introduction Section, we have briefly described other related published works [14][15][16][17]35,36,39]. Some of them analyse and compare the efficiency of some architectures studied in our work, such as ResNet50 [15,16,36,39], VGG19 [16,36], Xception [36], ViT and Swin Transformer [35,36], DeiT, ResMLP, and CaiT [35].…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the datasets used in the training and testing stages, each work uses and combines them differently. Some datasets are public and match those used in our work, e.g., Drishti-GS1 [14][15][16]35,36], Rim-ONE DL or some of its earlier versions [16,35,36,39], and Refuge [35,36], but others are different and, in some cases, private (ACRIMA [14,35,39], ORIGA [14,17,35,39], SCES [17], HVD [14], OHTS [39], HRF [15,35], DRIONS-DB [15], LAG [35,39], ESPERANZA [16], ODIR 5K [35]). Taking all this into account, we will now compare our results with those of these works indicatively.…”

Section: Discussionmentioning

confidence: 99%

“…In relation to glaucoma, the performance of three well-known CNNs is studied in [14]. The three best-fit models have very similar performance, offering accuracy values around 98% for a particular dataset.…”

Section: Introductionmentioning

confidence: 99%

“…When this architecture is adapted to work with In medicine, the application of CNNs can be found in several fields: skin cancer [6], lung diseases [7], heart diseases [8], breast cancer [9], vascular diseases [10], etc. In the case of Ophthalmology, these CNNs have been widely used for the diagnosis of diabetic retinopathy [11], macular degeneration [12], cataracts [13], and glaucoma [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images

Alayón,

Hernández,

Fumero

et al. 2023

Applied Sciences

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Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images

Alayón,

Hernández,

Fumero

et al. 2023

Applied Sciences

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“…Glaucoma detection & optimizing bio-inspired EABIFPA DL methods reviews was carried out, where the comparative analysis was done between the traditional and deep learning models (9,10) . QoS-RABCRP, DCNN and DL-GD (11)(12)(13) models were introduced to optimize and filter the data in a robust way. These models initially filter the image in the first stage for denoising and then segment it for the identification and classification processes automatically.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Identification in Digital Fundus Images using Deep Learning Enhanced Auto Encoder Networks (DL-EAEN) for Accurate Diagnosis

Sathya,

Balamurugan

2023

IJST

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Objectives: To propose a novel method to enhance glaucoma identification by leveraging Digital Fundus Images (DFI). The deep learning-based approach, along with feature detection techniques, is utilized to discover the built-in features of the DFI in an unsupervised manner to enable robust detection with high accuracy. Methods: The Enhanced Auto Encoder Networks (DL-EAEN) approach is used to evaluate the latent representations from DFI and identify the morphological changes associated with glaucoma for prompt identification and classification. The fundus images are utilized for optic disc localization and glaucoma detection, and the Scale-Invariant Feature Transform (SIFT) approach is used to identify the local features as well as significant spots in the images. The PAPILA retinal dataset is used for this study with a record of 244 patients, which includes 488 fundus images of the left and right eye of all the patients in the M & F category with clinical results of healthy, glaucoma, suspect, and eye with crystalline and with IOL. In order to construct pixel-level masks and define the outer edges of the optic cup, U-Net and Mask-R-CNN techniques are employed as an image segmentation process. To measure the performance of DL-EAEN, MATLAB software is used and compared against existing models such as SVM, Adaboost, and CNN-Softmax classifiers. Findings: The proposed deep learning based enhanced AEN method outperforms the prevailing methods of SVM, Adaboost, and CNN-Softmax classifiers with promising results of 95.6% accuracy, 0.8 dice-score, 96.2% sensitivity, 97.01% specificity, 97.08% F-score, 97.41% precision, 98.02% recall, and AUC-ROC with 0.89 TPR & 0.16 FPR. Novelty: The evident results of DL-EAEN shows accurate and consistent rate in glaucoma detection and classification, which helps ophthalmologists, make easy diagnosis. In terms of accuracy, dice score, AUC-ROC, sensitivity, specificity, precision and F-score, the DL-EAEN overcomes the limitations of existing models SVM, Adaboost, and CNN-Softmax classifiers.

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RDD-Net: Randomized Joint Data-Feature Augmentation and Deep-Shallow Feature Fusion Networks for Automated Diagnosis of Glaucoma

Tang,

Zhang,

Feng

2024

Lecture Notes in Computer Science

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Automatic Glaucoma Detection from Fundus Images Using Deep Convolutional Neural Networks and Exploring Networks Behaviour Using Visualization Techniques

Cited by 7 publications

References 46 publications

Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images

Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images

Glaucoma Identification in Digital Fundus Images using Deep Learning Enhanced Auto Encoder Networks (DL-EAEN) for Accurate Diagnosis

RDD-Net: Randomized Joint Data-Feature Augmentation and Deep-Shallow Feature Fusion Networks for Automated Diagnosis of Glaucoma

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