CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

Tabassum, Munazza; Khan, Tariq M.; Arsalan, Muhammad; Naqvi, Syed S.; Ahmed, Mansoor; Madni, Hussain Ahmed; Mirza, Jawad

doi:10.1109/access.2020.2998635

Cited by 59 publications

(29 citation statements)

References 58 publications

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“…Consequently, the acquired datasets are preprocessed to enhance their quality. Next, segmentation techniques are utilized to extract the foreground from the background pixels based on their distinguishable characteristics, e.g., color, texture and shape [24]- [26]. Lastly, WBCs are distinguished from RBCs through classifiers whereby the presence of nucleus is first determined from the color information of the cells.…”

Section: Methodsmentioning

confidence: 99%

Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.INDEX TERMS Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), computer-aided diagnostic (CAD), Leukemia.

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Section: Methodsmentioning

confidence: 99%

Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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“…In [36], joint segmentation of OD and OC is formulated as a semantic pixel-wise labeling problem. An encoderdecoder network called Cup Disc Encoder Decoder Network (CDED-Net) based on the fully connected SegNet [37] architecture is proposed, employing feature information reuse to bridge the semantic gap between the features shared between the encoder and decoder blocks.…”

Section: Related Workmentioning

confidence: 99%

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection

Ganesh

Kannayeram

Karthick

et al. 2021

Computational and Mathematical Methods in Medicine

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Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.

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“…Precision = T P T P + FP (7) iii) Recall is the ratio of TP and the correct pixels in OD according to ground truth.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The study confirmed that the accurate segmentation process of OD and OC jointly is essential to glaucoma detection and diagnosis. The authors of [7] leveraged the multi-task learning and dense connections to segment the OD and OC jointly. They evaluated their work on three datasets: DRISHTI-GS, RIM-ONE, and REFUGE.…”

Section: Introductionmentioning

confidence: 99%

Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

Ali

Abdel-Nasser

Jabreel³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

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CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

Cited by 59 publications

References 58 publications

Automated Diagnosis of Leukemia: A Comprehensive Review

Automated Diagnosis of Leukemia: A Comprehensive Review

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection

Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

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