10 research outputs found
Hypertensive Retinopathy Detection in Fundus Images Using Deep Learning-Based Model - Shallow ConvNet
Background: Hypertensive Retinopathy (HR) is amongst the abnormalities occurred with high blood pressure. This high blood pressure level makes retinal arterial narrower, retinal hemorrhages and cotton wool spots more harmful. Based on what was mentioned, early detection of hypertensive retinopathy is pivotal to prevent its following disabilities and boost its treatment with more accurate methods.
Material and Methods: The main objective of this study is to investigate an appropriate deep learning method for improving the automatic diagnosis of hypertensive retinopathy in its early stages. The complete data used in this study have been obtained from integration of Structured Analysis of the Retina (STARE) and The Digital Retinal Images for Vessel Extraction (DRIVE) datasets.
Results: Interestingly, we reached an accuracy of 87.5 % after using the well-suited preprocessing method to integrate different images for further analysis by our designed convolutional neural network (CNN).
Conclusion: This model performs well with integration of two mentioned datasets
RGB-D Salient Object Detection: A Survey
Salient object detection (SOD), which simulates the human visual perception
system to locate the most attractive object(s) in a scene, has been widely
applied to various computer vision tasks. Now, with the advent of depth
sensors, depth maps with affluent spatial information that can be beneficial in
boosting the performance of SOD, can easily be captured. Although various RGB-D
based SOD models with promising performance have been proposed over the past
several years, an in-depth understanding of these models and challenges in this
topic remains lacking. In this paper, we provide a comprehensive survey of
RGB-D based SOD models from various perspectives, and review related benchmark
datasets in detail. Further, considering that the light field can also provide
depth maps, we review SOD models and popular benchmark datasets from this
domain as well. Moreover, to investigate the SOD ability of existing models, we
carry out a comprehensive evaluation, as well as attribute-based evaluation of
several representative RGB-D based SOD models. Finally, we discuss several
challenges and open directions of RGB-D based SOD for future research. All
collected models, benchmark datasets, source code links, datasets constructed
for attribute-based evaluation, and codes for evaluation will be made publicly
available at https://github.com/taozh2017/RGBDSODsurveyComment: 24 pages, 12 figures. Has been accepted by Computational Visual Medi