A Semantic-based Scene segmentation using convolutional neural networks

Shaaban, Aya M.; Salem, Nancy M.; Al-Atabany, Walid

doi:10.1016/j.aeue.2020.153364

Cited by 11 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In experiment 1, our proposed model, MAs-FC-DenseNet, is compared against the traditional FC-DenseNet models including FC-DenseNet56 [11] and FC-DenseNet67 [11]. In experiment 2, our proposed model is compared against other end-to-end models, including DeeplabV3+ [20] and PSPNet [21] models.…”

Section: Resultsmentioning

confidence: 99%

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Wang¹,

Yao²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a common eye disease, which leads cause of blindness all around the world. Microaneurysms (MAs) is one of the early symptoms of DR. Accurate and effective MAs detection and segmentation is an important step for the diagnosis and treatment of DR. In this paper, we propose an automatic model for detection of MAs in fluorescein fundus angiography (FFA) images. The model mainly consists of two steps. The first step is pre-processing of FFA images, where the quality of FFA images is improved by Histogram Stretching and Gaussian Filtering algorithm. The second step is to detect MAs regions, where the MAs regions are detected by improved FC-DenseNet. We compare the proposed model with traditional FC-DenseNet model and other previously published models. The experimental result shows that our proposed model has the highest scores on evaluation metrics of pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1) and mean intersection over union (MIoU), which are 99.97%, 94.19%, 88.40%, 89.70%, 88.98% and 90.14%, respectively. The result suggests that the performance of our proposed model is closer to the ground truth of MAs detection. Our proposed model would be helpful for ophthalmologists to find the symptoms more quickly and to take better treatment measures in the screening process of diabetic retinopathy.

show abstract

Section: Resultsmentioning

confidence: 99%

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Wang¹,

Yao²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…CNN and ResNet could obtain higher-level features from the upper layer and give up the features of the lower layer, but these model may loss parts of small-size targets 6 , 7 . DeeplabV3+and PSPNet use spatial pyramid pooling module to further extract contextual information and improve the detection accuracy of small-size targets, but they have misdetection and missed detection problems for MA detection 8 , 9 . Some improvements on neural network have been used for MA detection.…”

Section: Introductionmentioning

confidence: 99%

A new detection model of microaneurysms based on improved FC-DenseNet

Wang

Yao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.

show abstract