scholarly journals Diabetic Retinopathy: Severity Level Classification based on Object Detection (Microaneurysms, Hemorrhages, and Hard Exudates) using Mathematical Morphology and Neural Networks

2018 ◽  
Author(s):  
Fifi Diah Rosalina ◽  
Dian C. Rini Novitasari ◽  
Ahmad Hanif Asyhar ◽  
Abdulloh Hamid ◽  
Muhammad Firmansjah
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Object Detection ◽  
Mathematical Morphology ◽  
Severity Level ◽  
Hard Exudates

scholarly journals Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices

2021 ◽  
Author(s):  
Aditya Jyoti Paul
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Recent Literature ◽  
Vision Impairment ◽  
Huge Number ◽  
Hard Exudates ◽  
Two Stages ◽  
Macular Ischemia

Diabetic Retinopathy (DR) is a severe complication that may lead to retinal vascular damage and is one of the leading causes of vision impairment and blindness. DR broadly is classified into two stages - non-proliferative (NPDR), where there are almost no symptoms, except a few microaneurysms, and proliferative (PDR) involving a huge number of microaneurysms and hemorrhages, soft and hard exudates, neo-vascularization, macular ischemia or a combination of these, making it easier to detect. More specifically, DR is usually classified into five levels, labeled 0-4, from 0 indicating no DR to 4 which is most severe. This paper firstly presents a discussion on the risk factors of the disease, then surveys the recent literature on the topic followed by examining certain techniques which were found to be highly effective in improving the prognosis accuracy. Finally, a convolutional neural network model is proposed to detect all the stages of DR on a low-memory edge microcontroller. The model has a size of just 5.9 MB, accuracy and F1 score both of 94% and an inference speed of about 20 frames per second.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

scholarly journals Intravitreal aflibercept partially reverses severe non-proliferative diabetic retinopathy in treatment-naïve patients

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098536
Author(s):  
Yuan Tao ◽  
Pengfei Jiang ◽  
Min Liu ◽  
Ying Liu ◽  
Lihua Song ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Intravitreal Injection ◽  
Proliferative Diabetic Retinopathy ◽  
Posterior Pole ◽  
Foveal Thickness ◽  
Hard Exudates ◽  
Central Foveal Thickness ◽  
Treatment Naïve ◽  
The Mean ◽  
Retinal Microaneurysms

Objective To evaluate whether diabetic retinopathy can be reversed after aflibercept, based on improvements in diabetic macular edema, hard exudates (HEs) of the posterior pole, and retinal microaneurysms (MAs). Methods This was a single-center retrospective study of 30 patients (34 eyes) with severe non-proliferative diabetic retinopathy (NPDR) who were treated between August and October 2018. Best-corrected visual acuity (BCVA), central foveal thickness (CFT), area of HEs, and number of MAs were compared before and after treatment. Results The mean patient age was 61.4 ± 7.1 years; 14 patients (46.7%) were men. The mean number of injections per patient was 3.5 ± 0.5. The time between the last injection and the last follow-up was 82 days (range, 78–110 days). Six months after the first intravitreal injection, significant improvement was observed in BCVA (from 0.70 ± 0.18 to 0.42 ± 0.19 logMAR), CFT (from 377.17 ± 60.41 to 261.21 ± 31.50 µm), and number of MAs (from 182.2 ± 77.4 to 101.5 ± 59.6). Observations over 6 months after the first intravitreal injection showed a statistically significant reduction in the area of HEs (P = 0.007). No adverse events occurred during the treatment period. Conclusion Diabetic retinopathy might be partially reversed by aflibercept treatment, as indicated by BCVA, CFT, number of MAs, and area of HEs.

Grading Diabetic Retinopathy Severity Using Modern Convolution Neural Networks (CNN)

2021 ◽  
Author(s):  
Andrew Lee ◽  
Matloob Khushi ◽  
Patrick Hao ◽  
Shahadat Uddin ◽  
Simon K. Poon
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Convolution Neural Networks
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