Automated detection of Glaucoma using deep learning convolution network (G-net)

Juneja, Mamta; Singh, Shaswat; Agarwal, Naman; Bali, Shivank; Gupta, Shubham; Thakur, Niharika; Jindal, Prashant

doi:10.1007/s11042-019-7460-4

Cited by 96 publications

(36 citation statements)

References 31 publications

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“…The proposed method has been tested on two datasets which are Standard Analysis of the Retina, STARE (81 images, 605 x 700 pixels) [7], and Retinopathy of Prematurity, ROP (91 images, 640 x 480 pixels) which is a dataset collected to detect the sign of retinopathy of prematurity from infant patients [1]. The images in two data sets include diabetic retinopathy symptoms, such as exudates, hemorrhages, and other OD abnormal appearances.…”

Section: Methodsmentioning

confidence: 99%

“…There are numerous approaches to segmentation, some of them are discussed below, (1). Edge detection: This is an approach where it is imperative to detect the boundaries of regions present within the image.…”

Section: Segmentation Of Imagesmentioning

confidence: 99%

“…All retinal structures can be examined analyzed with high modern optical equipment and lenses. Using such equipment gives a view of the optic disk (OD) [1] and an indication of the health of human retina. In particular, the ophthalmologists analyses the color, sharpness of edge, cupping size, swelling, hemorrhages, notching in the OD and other abnormalities.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Kanagala¹,

Krishnaiah²

2020

ISI

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The leading eye disease commonly humans suffering is Glaucoma, which cause vision loss by progressively affecting peripheral vision. Presently, glaucoma diagnosis is performed by ophthalmologists, doctors, human professionals with the help of different medical equipment to analyze and understand for identifying the problematic eye images. Detecting glaucoma is a difficult task from the eye images with the help of existing data mining techniques. In this paper, we present an approach for diagnosing of glaucoma with the help of deep learning based techniques fundus images that are analyzed by making use of stateof-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis using Convolutional Neural Networks (CNNs) and Varational auto encoder are used. The performance results show the comparative performance of proposed mechanism with state of art mechanisms.

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Section: Methodsmentioning

confidence: 99%

Section: Segmentation Of Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Kanagala¹,

Krishnaiah²

2020

ISI

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“…Ayushi et al extracted region of interest by passing the desired window across columns and rows for further processing [17] and Zilly et al performed pre-processing on input image using cropping and down sampling of retinal image followed by conversion of RGB to Lab color space and finally applied normalization to standardize the retinal images for further processing [18]. More recently, Juneja et al utilized red, green and blue channels for segmentation [19].…”

Section: Channel Separation [6]mentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

Juneja

2020

Wireless Pers Commun

Self Cite

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Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras. But the acquired images may be prone to certain outliers that create hindrance in diagnosis of glaucoma by tempering the accuracy. These outliers include retinal vessels, low contrast of images and uneven illumination that deteriorates the performance of disc and cup segmentation which are the key indicators to diagnose glaucoma. Thus, pre-processing of retinal images to remove outliers plays a significant role in diagnosis. This paper presents an approach for pre-processing the retinal fundus image followed by its comparison with state of the art. Based on the experimental analysis the performance of the proposed approach is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index. Further, the proposed approach has been compared with state of the art using metrics such as Jaccard index and dice similarity on the basis of segmentation outcomes on different pre-processing approaches.

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“…Sumanth 16 initiated a model for a timolol maleate‐controlled release ocular drug deliverance scheme for the cure of glaucoma. Juneja et al, 17 proposed an automated approach based on deep convolution neural network architecture for the segmentation of OC and OD. For OC segmentation, a disc metric and Jaccard index of 95.05% and 90.62% are obtained.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation algorithm with deep learning framework for early detection of glaucoma

Natarajan¹,

Esakkirajan

Keerthiveena

et al. 2021

Concurrency and Computation

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Early stage of diagnosis of eye diseases through automatic analysis in the retinal image is the emerging technology in the area of retinopathy. Glaucoma is the primary reason for the loss of visibility in people around the world. The separation of the disc and the cup in the optic region is the technique used to identify glaucoma in the human retinal image. In this paper, superpixel segmentation, followed by Modified Kernel Fuzzy C‐Means (MKFCM) algorithm is used to segment the optic disc and optic cup. The proposed segmentation method achieves a maximum average of F‐score as 0.979, an average boundary distance as 10.016 pixels, and an average correlation coefficient of 0.949. To train convolutional neural networks (CNN), the segmented images obtained by the MKFCM segmentation algorithm is given as the input for the identification of glaucoma. This CNN uses the gray level co‐occurrence matrix features calculated from the segmented image. The experiment used for this study demonstrates that CNN gives superior categorization correctness and requires fewer figures of knowledge iterations than the original CNN. The accuracy obtained by this proposed method is 94.2%. The model will help to identify the proper class of severity of glaucoma in retinal images.

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Automated detection of Glaucoma using deep learning convolution network (G-net)

Cited by 96 publications

References 31 publications

Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Pre-processing of Retinal Images for Removal of Outliers

Automated segmentation algorithm with deep learning framework for early detection of glaucoma

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