Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma

Ferreira, Marcos Vinícius dos Santos; Filho, Antônio Oséas de Carvalho; Sousa, Alcilene Dalília de; Silva, Aristófanes Corrêa; Gattass, Marcelo

doi:10.1016/j.eswa.2018.06.010

Cited by 65 publications

(23 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3. Dos Santos Ferreira et al [63] developed an architecture based on U-net [59] solely to perform the OD segmentation. In this way, the net does not have a fully connected layer.…”

Section: Discussionmentioning

confidence: 99%

“…Figures 4 and 5 point out a graph with all these metrics. When training a model in the red channel, Dos Santos Ferreira et al [63] obtained 100% in the three measures. However, in other channels as the blue channel, the Acc and Sn values were of 94% and 80%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, the most significant advantage of cross-validation is that instead of utilizing just one test set to validate the model, many other sets are created from the same data. Li et al [55] Fu et al [58] Raghavendra et al [62] dos Santos Ferreira et al [63] Christopher et al [65] Chai et al […”

Section: Discussionmentioning

confidence: 99%

“…About 70% of the data was randomly adopted for training, and 30% for testing. 19:20 In addition, dos Santos Ferreira et al [63] schemed a method for both image segmentation and classification in 2018. First, a U-net convolutional network was trained to perform the OD segmentation.…”

Section: Methods Using Deep Convolutional Network Architectures For Gmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

View full text Add to dashboard Cite

Introduction: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. Methods:The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized.Discussion: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques.Conclusions: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.

show abstract

“…3. Dos Santos Ferreira et al [63] developed an architecture based on U-net [59] solely to perform the OD segmentation. In this way, the net does not have a fully connected layer.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methods Using Deep Convolutional Network Architectures For Gmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This has been done in research on the classification of images containing blood. This includes Ferreira et al, who used CNN and a texture descriptor for detection and diagnosis of glaucoma [19], while Tiwari et al used CNN for classifying white blood cell type [20]. Vogado et al used pre-trained state-of-theart CNN models and Support Vector Machines for diagnosing leukemia in blood slides [21].…”

Section: Introductionmentioning

confidence: 99%

Vision System for Quality Assessment of Robotic Cleaning of Fish Processing Plants Using CNN

Bjørlykhaug

Egeland

2019

IEEE Access

View full text Add to dashboard Cite

A vision system has been developed for automatic quality assessment of robotic cleaning of fish processing lines. The quality assessment is done by detecting residual fish blood on cleaned surfaces. The system is based on classification using convolutional neural networks (CNNs). The performance of different convolutional neural network architectures and parameters is evaluated. The datasets that simulate various conditions in fish processing plants are generated using data augmentation techniques. Tests using further augmented training data to increase the performance of the neural network are performed, which results in a substantial increase in performance both compared to the color thresholding technique and the same neural network architecture without augmented training data. The performance of the system is validated in experiments in an industrial setting. INDEX TERMS Aquaculture, robot vision system, machine learning, computer vision.

show abstract

A Novel Approach for Early Intervention of Retinal Disorders Using Machine Learning Techniques

Dhanusha¹,

Muthukumar²,

Lakshmi³

2022

Inventive Systems and Control

View full text Add to dashboard Cite

Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma

Cited by 65 publications

References 21 publications

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Vision System for Quality Assessment of Robotic Cleaning of Fish Processing Plants Using CNN

A Novel Approach for Early Intervention of Retinal Disorders Using Machine Learning Techniques

Contact Info

Product

Resources

About