scholarly journals Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures

2021 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Muhammad Arsalan ◽  
Adnan Haider ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Experimental Results ◽  
Health Study ◽  
Retinal Vasculature ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Retinal Disorders ◽  
Structured Analysis ◽  
Vessel Extraction

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method’s performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

scholarly journals Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3454 ◽  
Author(s):  
Muhammad Arsalan ◽  
Na Rae Baek ◽  
Muhammad Owais ◽  
Tahir Mahmood ◽  
Kang Ryoung Park
Keyword(s):  
Deep Learning ◽  
Retinitis Pigmentosa ◽  
Low Cost ◽  
Semantic Segmentation ◽  
Disease Diagnosis ◽  
Vital Role ◽  
Hypertensive Retinopathy ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Medical Practitioners

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

scholarly journals Hemodynamics in the retinal vasculature during the progression of diabetic retinopathy

2017 ◽  
Vol 1 (4) ◽  
pp. 6-15
Author(s):  
Francesco Calivá ◽  
Georgios Leontidis ◽  
Piotr Chudzik ◽  
Andrew Hunter ◽  
Luca Antiga ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Blood Flow ◽  
Diabetic Retinopathy ◽  
Fluid Dynamic ◽  
First Year ◽  
Retinal Vasculature ◽  
Fundus Images ◽  
Parent Vessel ◽  
Last Stage ◽  
Vascular Trees

Purpose: In this study, it is shown that hemodynamic features are applicable as biomarkers to evaluate the progression of diabetic retinopathy (DR). Methods: Ninety-six fundus images from twenty-four subjects were selected. For each patient, four photographs were captured during the three years before DR and in the first year of DR. The vascular trees, which consisted of a parent vessel and two child branches were extracted, and at the branching nodes, the fluid dynamic conditions were estimated. Results: Veins were mostly affected during the last stage of diabetes before DR. In the arteries, the blood flow in both child branches and the Reynolds number in the smaller child branch were mostly affected. Conclusion: This study showed that hemodynamic features can add further information to the study of the progression of DR.

Morphometric Measurements of The Retinal Vasculature in Fundus Images with Vampire

2015 ◽  
pp. 91-111 ◽  
Author(s):  
Emanuele Trucco ◽  
Andrea Giachetti ◽  
Lucia Ballerini ◽  
Devanjali Relan ◽  
Alessandro Cavinato ◽  
...  
Keyword(s):  
Retinal Vasculature ◽  
Fundus Images ◽  
Morphometric Measurements

scholarly journals Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

2019 ◽  
Vol 8 (9) ◽  
pp. 1446 ◽  
Author(s):  
Arsalan ◽  
Owais ◽  
Mahmood ◽  
Cho ◽  
Park
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Images ◽  
Computer Assisted ◽  
Hypertensive Retinopathy ◽  
Retinal Vessels

Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.

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