<scp>TWEEC</scp>: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid, Lamiaa

doi:10.1002/ima.22621

Cited by 12 publications

(6 citation statements)

References 79 publications

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“…Both these works showed that their deep learning methods outperformed various TL-based approaches from literature. These findings are in agreement with previous research that showed that in medical applications, a well-developed deep network can significantly outperform generic TL based methods [ 3 , 43 ].…”

Section: Literature Reviewsupporting

confidence: 93%

“…Accordingly, the superiority of the proposed handcrafted L1-Contourlet features can be attributed to the relevance of the textural and statistical features computed from the different contourlet subbands. Deep pretrained networks commonly employed in medical applications are designed without consideration of the characteristics of the medical images or disease symptoms [ 3 , 43 ]. Features extracted by these generic deep networks might thus inefficiently represent the different classes resulting in less-than-optimal classification performance.…”

Section: Discussionmentioning

confidence: 99%

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Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms

Abdel-Hamid

2023

Multimed Tools Appl

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Chest computer tomography (CT) provides a readily available and efficient tool for COVID-19 diagnosis. Wavelet and contourlet transforms have the advantages of being localized in both space and time. In addition, multiresolution analysis allows for the separation of relevant image information in the different subbands. In the present study, transform-based features were investigated for COVID-19 classification using chest CT images. Several textural and statistical features were computed from the approximation and detail subbands in order to fully capture disease symptoms in the chest CT images. Initially, multiresolution analysis was performed considering three different wavelet and contourlet levels to determine the transform and decomposition level most suitable for feature extraction. Analysis showed that contourlet features computed from the first decomposition level (L1) led to the most reliable COVID-19 classification results. The complete feature vector was computed in less than 25 ms for a single image having of resolution 256 × 256 pixels. Next, particle swarm optimization (PSO) was implemented to find the best set of L1-Contourlet features for enhanced performance. Accuracy, sensitivity, specificity, precision, and F-score of a 100% were achieved by the reduced feature set using the support vector machine (SVM) classifier. The presented contourlet-based COVID-19 detection method was also shown to outperform several state-of-the-art deep learning approaches from literature. The present study demonstrates the reliability of transform-based features for COVID-19 detection with the advantage of reduced computational complexity. Transform-based features are thus suitable for integration within real-time automatic screening systems used for the initial screening of COVID-19.

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Section: Literature Reviewsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms

Abdel-Hamid

2023

Multimed Tools Appl

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“…Shinde [18] captured the ROI of the input with the bright-spot approach and segmented by the U-Net for structural features. Recently, a two-branched DL network (TWEEC) is proposed by [19] for the classification of denoised retinal images in wavelet domain. In this study, both CNN branches were designed with similar architecture.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma classification using a polynomial-driven deep learning approach

Naidana¹,

Barpanda²

2023

Bulletin EEI

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In this paper, a deep learning-based multi-stage polynomial driven glaucoma classification-net (PDGC-Net) has been proposed for glaucoma identification through retinal images. The proposed approach begins with retinal image pu[1]rification by noise estimation and reduction. Noise has been estimated using a polynomial coefficient-based approach. Images are classified using PDGC-Net, whose polynomial indeterminate representative blocks are designed using new convolutional neural networks (CNN) architectures. The performance of PDGC[1]Net has been observed on the ACRIMA, ORIGA, and retinal image database for optic nerve evaluation (RIM-ONE) datasets. The experimentation is carried out on noisy and denoised images separately, and PDGC-Net has achieved 96% to 98% and 98% to 100% accuracy ranges, respectively. The model’s elasticity is tested with various stages of PDGC-Net. The quantitative PDGC-Net perfor[1]mance analysis is done with state-of-the-art CNN models. The proposed model’s performance has been proven and could be an effective aid to ophthalmologists for glaucoma screening (GS).

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“…Furthermore, the size and geometry of the outer perimeter of the OD can be used to diagnose glaucoma. 3,4 Retinal vessels consist of venules and arterioles, which show up in retinal imaging as long, branched structures emerging from the OD. At least four billion people are estimated to suffer from vision impairment or blindness worldwide.…”

Section: Introductionmentioning

confidence: 99%

“…For approximate macula and fovea detection, which is responsible for the keenest vision, the position and radius of the OD can be considered as references. Furthermore, the size and geometry of the outer perimeter of the OD can be used to diagnose glaucoma 3,4 . Retinal vessels consist of venules and arterioles, which show up in retinal imaging as long, branched structures emerging from the OD.…”

Section: Introductionmentioning

confidence: 99%

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

Prakash

Kakarla

2023

Int J Imaging Syst Tech

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Fundus disorders like Glaucoma are a serious health risk that affects people all over the world, lowering their quality of life. Optic disc (OD) segmentation is a crucial step for automatic detection of glaucoma with fundus images. This paper presents an efficient and accurate OD segmentation methodology from eye fundus images. In the proposed methodology, fundus images are transformed from RGB (Red, Green, and Blue) color space to LAB (Luminance, A‐axis, B‐axis) color space. A new image is generated using the luminance, A‐axis component, and image information content. As a pre‐segmentation stage, the unsupervised approach extracts the region of interest containing OD. The adaptive threshold method, followed by morphological operations, extracts the contours of the OD region and removes the spur. Segmentation of OD is a challenging task due to similar intensity levels with neighborhood, vascular occlusion, and retinal atrophy. The proposed work overcomes the challenges by segmentation of the OD at three levels, which enhances the functionality at different illumination conditions. The features are extracted at various levels to determine whether the segmented image contains the OD or not. The efficiency of the proposed methodology demonstrates by applying it to a variety of fundus images from datasets, namely ISBI IDRiD, DRISHTI‐GS, and SYSU. Key performance metrics like Intersection over Union and Dice Coefficient are used to evaluate the performance of the proposed methodology. The proposed methodology achieves Intersection over Union of 0.996, 0.997, and 0.994 on ISBI IDRiD, DRISHTI‐GS, and SYSU datasets, respectively. Similarly, the proposed methodology obtains 0.96, 0.97, and 0.98 Dice Coefficient on the three datasets.

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TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Cited by 12 publications

References 79 publications

Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms

Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms

Glaucoma classification using a polynomial-driven deep learning approach

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

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