A new convolution neural network model “KR-NET” for retinal fundus glaucoma classification

Sonti, Kamesh

doi:10.1016/j.ijleo.2023.170861

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…In the second stage, a CNN is used to classify normal and glaucoma. Kamesh et al [23] presented glaucoma detection using a CNN architecture. Their methodology involves ROI selection.…”

Section: Classification Of Glaucomamentioning

confidence: 99%

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Almeshrky,

Karacı

2024

Applied Sciences

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Glaucoma is a common eye disease that damages the optic nerve and leads to loss of vision. The disease shows few symptoms in the early stages, making its identification a complex task. To overcome the challenges associated with this task, this study aimed to tackle the localization and segmentation of the optic disc, as well as the classification of glaucoma. For the optic disc segmentation, we propose a novel metaheuristic approach called Grey Wolf Optimization (GWO). Two different approaches are used for glaucoma classification: a one-stage approach, in which the whole image without cropping is used for classification, and a two-stage approach. In the two-stage approach, the optic disc region is detected using the You Only Look Once (YOLO) detection algorithm. Once the optic disc region of interest (ROI) is identified, glaucoma classification is performed using pre-trained convolutional neural networks (CNNs) and vision transformation techniques. In addition, both the one-stage and the two-stage approaches are applied in combination with the pre-trained CNN using the Random Forest algorithm. In segmentation, GWO achieved an average sensitivity of 96.04%, a specificity of 99.58%, an accuracy of 99.39%, a DICE coefficient of 94.15%, and a Jaccard index of 90.4% on the Drishti-GS dataset. For classification, the proposed method achieved remarkable results with a high-test accuracy of 100% and 88.18% for hold-out validation and three-fold cross-validation for the Drishti-GS dataset, and 96.15% and 93.84% for ORIGA with hold-out and five-fold cross-validation, respectively. Comparing the results with previous studies, the proposed CNN model outperforms them. In addition, the use of the Swin transformer shows its effectiveness in classifying glaucoma in different subsets of the data.

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“…In the second stage, a CNN is used to classify normal and glaucoma. Kamesh et al [23] presented glaucoma detection using a CNN architecture. Their methodology involves ROI selection.…”

Section: Classification Of Glaucomamentioning

confidence: 99%

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Almeshrky,

Karacı

2024

Applied Sciences

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“…The authors have further applied attention mechanisms to the ResNet18 model and recognized gaze maps in the diabetic retinopathy images. The authors (Sonti and Dhuli, 2023) proposed a 26-layer deep CNN architecture and performed classification of glaucoma in fundus images. With the proposed architecture, the authors achieved a classification accuracy of 96.70% on the ACRIMA dataset (Khaparde et al.…”

Section: Related Workmentioning

confidence: 99%

“…The authors tested their approach on the OIA-ODIR dataset and achieved a classification accuracy of 92.41% (Thanki, 2023), proposed a hybrid approach for the classification of normal images and glaucoma images. (Sonti and Dhuli, 2023) proposed a 26-layer deep CNN architecture and performed classification of glaucoma in fundus images. With the proposed architecture, the authors achieved a classification accuracy of 96.70% on the ACRIMA dataset (Khaparde et al, 2023), developed a fundus eye disease classification mechanism by employing a deep learningbased Swin UNET-Segmentation architecture.…”

Section: Related Workmentioning

confidence: 99%

“…To validate the proposed model, we performed tests on the eye disease datasets used in the related work. As an effort to perform tests on the datasets used in the related work, we selected the OIA-ODIR (Al-Fahdawi et al, 2024) and ACRIMA (Sonti and Dhuli, 2023) datasets and computed the metric mAP by utilizing the weights obtained by training the proposed EYE-YOLO model on the created eye disease detection dataset as used in this work. From the OIA-ODIR dataset, we selected image samples for cataract, glaucoma and normal eye and from the ACRIMA dataset, the image samples for glaucoma and normal eye were selected.…”

Section: Ijicc 173mentioning

confidence: 99%

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EYE-YOLO: a multi-spatial pyramid pooling and Focal-EIOU loss inspired tiny YOLOv7 for fundus eye disease detection

Kumar,

Dhanalakshmi

2024

IJICC

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PurposeThe purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approachThe approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).FindingsThe proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.Originality/valueThis work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

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Ophthalmic image processing for disease detection

El-hales,

El-Samie,

Dessouky

et al. 2024

J Opt

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A new convolution neural network model “KR-NET” for retinal fundus glaucoma classification

Cited by 9 publications

References 17 publications

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

EYE-YOLO: a multi-spatial pyramid pooling and Focal-EIOU loss inspired tiny YOLOv7 for fundus eye disease detection

Ophthalmic image processing for disease detection

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