Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications

Galveia, José; Travassos, António; Quadros, Francisca Agra de Almeida; Cruz, Luís A. da Silva

doi:10.1007/978-3-319-65981-7_10

Cited by 7 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 1, human eye is divided into two major parts: anterior segment and posterior segment [1]. Anterior segment mainly consists of iris, cornea, lens, pupil, anterior chamber and ciliary muscle.…”

Section: Dry Eye Disease and Its Clinical Diagnosismentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

Vyas¹,

Mehta²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Dry Eye Disease (DED) is one of the commonly occurring chronic disease today, affecting the vision of eye. It causes severe discomfort in eye, visual disturbance and blurred vision impacting the quality of life of patients. Due to recent advancements in Artificial Intelligence (AI) and rapid progress of analytics techniques, several image modality based computerized DED detection techniques are available in literature. These techniques transform image data into real and actionable insights and thus, provide new understanding and ways for better and quicker treatment. In addition, they assist ophthalmologist to diagnose dry eye disease precisely as well as to reduce the cost of healthcare. In this paper, we present a review on computerized DED detection techniques covering all available techniques. In addition, we present a parametric evaluation of these techniques based on our identified set of parameters. Though surveys on clinical DED detection techniques are available, this is the first attempt in literature to log down all computerized DED detection techniques at one place. Our survey will assist researchers and ophthalmologists to understand various technological advancements in the field of DED detection. As a little amount of work has been done in the field of automatizing dry eye disease detection, the field provides plenty of research opportunities to develop an automated efficient DED detection technique.

show abstract

Section: Dry Eye Disease and Its Clinical Diagnosismentioning

confidence: 99%

“…Humans receive information of their surroundings through eyes. A transparent structure situated in front of the eye is referred as cornea [1][2][3]. In order to keep eyes healthy, cornea should be kept moist.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

Vyas¹,

Mehta²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Back propagation and gradient descent methods are commonly applied to train CNNs and to make them faster at learning and convergence. The main demerit of CNNs is that it needs a large amount of memory to store the results of the convolutional layer that finally forwards to the back propagation layer to compute gradients [143][144][145][146][147][148][149]. Some of the other variants of CNN networks, like Alexnet (https://github.com/BVLC/caffe/tree/master/models/bvlcalexnet), Lenet (http: //deeplearning.net/tutorial/lenet.html), faster R-CNN (https://github.com/ShaoqingRen/fasterrcnn), googlenet (https://github.com/BVLC/caffe/tree/master/models/bvlcgooglenet), Resnet (https://github.…”

Section: Latest Trendsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

show abstract

“…Spectral domain-optical coherence tomography (SD-OCT) is an image diagnosis technology developed rapidly in ophthalmology in recent years [3,4]. It has the characteristics of non-contact, high resolution, high reproducibility, fast image acquisition, and it can objectively quantitatively detect the thickness of each layer of the retina and monitor the occurrence, development and outcome of optic nerve ber damage [5,6].…”

Section: Introductionmentioning

confidence: 99%

Early Diagnostic Value of Spectral Domain-optical Coherence Tomography for Optic Nerve Injury in Sellar-occupied Patients

Zhang¹,

Yang²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: To investigate the value of spectral domain-optical coherence tomography (SD-OCT) in the early diagnosis of optic nerve injury in patients with occupying lesion in sellar region. Methods: A case-control study was conducted for patients who were diagnosed as the occupying lesion in sellar region in the Department of Ophthalmology. The general routine examination and visual field examination were performed in the ophthalmology department. A total of 22 patients(44 eyes) with no abnormalities were selected, and 22 healthy controls (44 eyes) were enrolled. SD-OCT was used to detect the average thickness of peripapillary retinal nerve fiber layer (pRNFL) and the thickness of four quadrants and the thickness of each layer of macular ganglion cell complex (mGCC). The results were compared to evaluate the damage of the optic nerve. The area under the receiver operating characteristic (ROC) curve (AUC) was evaluated by SD-OCT to detect the diagnostic efficacy of pRNFL and mGCC for sellar-occupying optic nerve damage. Results: The average thickness of pRNFL was significantly different from that of healthy controls (P<0.01). The mean thickness of mGCC, macular retinal nerve fiber layer (mRNFL) and GCL+ were significantly different from those of healthy controls (P<0.05). The AUC values of pRNFL, mRNFL, ganglion cell layer (GCL) + and mGCC were 0.757, 0.643, 0.702 and 0.688, respectively. The diagnostic value was pRNFL> GCL+ > mGCC > mRNFL. Conclusion: The thickness of pRNFL and mGCC detected by SD-OCT has clinical value in the diagnosis of optic nerve injury in early patients with occupying lesion in sellar region. The change of pRNFL thickness is more effective than mGCC thickness in the early sellar-occupying optic nerve injury.

show abstract

Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications

Cited by 7 publications

References 35 publications

A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Early Diagnostic Value of Spectral Domain-optical Coherence Tomography for Optic Nerve Injury in Sellar-occupied Patients

Contact Info

Product

Resources

About