Detection of glaucoma using retinal fundus images: A comprehensive review

Shabbir, Amsa; Rasheed, Aqsa; Shehraz, Huma; Saleem, Aliya; Zafar, Bushra; Sajid, Muhammad; Ali, Nouman; Dar, Saadat Hanif; Shehryar, Tehmina

doi:10.3934/mbe.2021106

Cited by 50 publications

(24 citation statements)

References 146 publications

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“…Clustering and classification results of the DFC-VAE based latent representations of optic disc images showed agreement with prior research on glaucoma assessment [26,27]. Readers should note that although CNN-based supervised deep learning methods exist for glaucoma analysis [13][14][15]28,29], they do not account for the explainability of the attributes of the optic disc, which are responsible for glaucoma. Given that most supervised learning methods are dependent on human graded labels, the learning process becomes blindsided, making glaucoma assessment error-prone.…”

Section: Discussionsupporting

confidence: 76%

“…Innovative ground truth generation methods such as OCT-acquired RNFL thickness measurements for DL have improved these shortcomings. Dubbed Machine to Machine (M2M), this technique has not only enhanced glaucoma diagnostic accuracy with fundus photos [13] but also provided a quantitative estimates that can be used to track progression [14]. Despite these developments, standard DL methods on fundus photos have limitations, such as variability in the quality of the images [13], underlying data distribution [13,14], and most importantly, interference due to non-glaucoma prominent features (different eye diseases) [15], which may cause hindrance in classification.…”

Section: Introductionmentioning

confidence: 99%

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Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders

Mandal¹,

Jammal²,

Medeiros³

2021

Preprint

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One of the leading causes of blindness is glaucoma, which is challenging to detect since it remains asymptomatic until the symptoms are severe. Thus, diagnosis is usually possible until the markers are easy to identify, i.e., the damage has already occurred. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. However, due to the nature of the disease, researchers still debate which markers qualify as a consistent glaucoma metric. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data. Although favorable, these methods make expert analysis difficult as they provide no insight into the model discrimination process. In this paper, we overcome this using deep generative networks, a deep learning model that learns complicated, high-dimensional probability distributions. We train a Deep Feature consistent Variational Autoencoder (DFC-VAE) to reconstruct optic disc images. We show that a small-sized latent space obtained from the DFC-VAE can learn the high-dimensional glaucoma data distribution and provide discriminatory evidence between normal and glaucoma eyes. Latent representations of size as low as 128 from our model got a 0.885 area under the receiver operating characteristic curve when trained with Support Vector Classifier.

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Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders

Mandal¹,

Jammal²,

Medeiros³

2021

Preprint

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“…Also the traditional machine learning models areused for glaucoma prediction [7][8][9].Researchers comprehensively reviewed in their different articles about glaucoma, its types, cause, effect, and possible treatments. They used clinically as well as image processing, machine learning, and deep learning techniques to detect this disease effectively [10][11]…”

Section: Related Workmentioning

confidence: 99%

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

Bandyopadhyay¹,

Bose²,

Dutta³

et al. 2021

Preprint

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In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

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“…ese CNNs possess a great deal of self-learning capability, adaptability, and generalization power and are heavily used in medical imaging problems and IoT-based systems [28,29]. Conventional image identification techniques need hand-crafted features extraction followed by categorization, while the CNN-based methods only require the image data which are given as an input to the network, and the task of image classification is achieved by their self-learning property [30].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning

Bukhari

Yasmin

Sammad

et al. 2022

Mathematical Problems in Engineering

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Leukemia is a fatal category of cancer-related disease that affects individuals of all ages, including children and adults, and is a significant cause of death worldwide. Particularly, it is associated with White Blood Cells (WBC), which is accompanied by a rise in the number of immature lymphocytes and cause damage to the bone marrow and/or blood. Therefore, a rapid and reliable cancer diagnosis is a critical requirement for successful therapy to raise survival rates. Currently, a manual analysis of blood samples obtained through microscopic images is done to diagnose this disease, which is often very slow, time-consuming, and less accurate. Furthermore, in microscopic analysis, the appearance and shape of leukemic cells seem very similar to normal cells which make detection more difficult. In the past decades, deep learning utilizing Convolutional Neural Networks (CNN) has provided state-of-the-art approaches for image classification problems; however, there is still a gap to improve their efficacy, learning procedure, and performance. Therefore, in this research study, we proposed a new variant of deep learning algorithm to diagnose leukemia disease by analyzing the microscopic images of blood samples. The proposed deep learning architecture emphasizes the channel associations on all levels of feature representation by incorporating the squeeze and excitation learning that recursively performs recalibration on channel-wise feature outputs by modeling channel interdependencies explicitly. In addition, the incorporation of the squeeze-and-excitation process enhances the feature discriminability of leukemic and normal cells, and strategically assists in exposing informative features of leukemia cells while suppressing less valuable ones as well as improving feature representational power of deep learning algorithm. We show that piling these learning operations of squeeze and excite together in a deep learning model can improve the performance of the model in diagnosing leukemia from microscopic images based on blood samples of patients. Furthermore, an extensive set of experiments are performed on both cropped cells and full-size microscopic images as well as with data augmentation to address the problem of fewer data and to further boost their performance. The proposed model is tested on two publicly available datasets of blood samples of leukemia patients, namely, ALL_IDB1 and ALL_IDB2. The suggested deep learning model exhibits good results and can be utilized to make a reliable computer-aided diagnosis for leukemia cancer.

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Detection of glaucoma using retinal fundus images: A comprehensive review

Cited by 50 publications

References 146 publications

Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders

Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning

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