scholarly journals Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus Images

2021 ◽  
Vol 7 (1) ◽  
pp. 22
Author(s):  
José Morano ◽  
Álvaro S. Hervella ◽  
Jorge Novo ◽  
José Rouco
Keyword(s):  
Deep Neural Networks ◽  
Fundus Images ◽  
Vascular Tree ◽  
Joint Task ◽  
Segmentation Approach ◽  
Color Fundus Images ◽  
Joint Classification ◽  
Vascular Trees ◽  
Arteries And Veins

The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases. An exhaustive analysis involves segmenting the retinal vessels and classifying them into veins and arteries. In this work, we present an accurate approach, based on deep neural networks, for the joint segmentation and classification of the retinal veins and arteries from color fundus images. The presented approach decomposes this joint task into three related subtasks: the segmentation of arteries, veins and the whole vascular tree. The experiments performed show that our method achieves competitive results in the discrimination of arteries and veins, while clearly enhancing the segmentation of the different structures. Moreover, unlike other approaches, our method allows for the straightforward detection of vessel crossings, and preserves the continuity of the arterial and venous vascular trees at these locations.

An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection

2019 ◽  
Vol 82 (4) ◽  
pp. 361-372 ◽  
Author(s):  
Hidayat Ullah ◽  
Tanzila Saba ◽  
Naveed Islam ◽  
Naveed Abbas ◽  
Amjad Rehman ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Ensemble Classification ◽  
Features Selection ◽  
Fundus Images ◽  
Color Fundus Images

Deep learning approach for classification of eye diseases based on color fundus images

2020 ◽  
pp. 25-57
Author(s):  
Bambang Krismono Triwijoyo ◽  
Boy Subirosa Sabarguna ◽  
Widodo Budiharto ◽  
Edi Abdurachman
Keyword(s):  
Deep Learning ◽  
Eye Diseases ◽  
Learning Approach ◽  
Fundus Images ◽  
Color Fundus Images

scholarly journals Multi-label classification of fundus images based on graph convolutional network

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Yinlin Cheng ◽  
Mengnan Ma ◽  
Xingyu Li ◽  
Yi Zhou
Keyword(s):  
Large Scale ◽  
Fundus Images ◽  
Convolutional Network ◽  
Hard Exudates ◽  
Proposed Model ◽  
Diabetic Population ◽  
Color Fundus Images ◽  
Aided Diagnosis ◽  
Large Scale Screening

Abstract Background Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in current research. Methods This study proposed a multi-label classification method based on the graph convolutional network (GCN), so as to detect 8 types of fundus lesions in color fundus images. We collected 7459 fundus images (1887 left eyes, 1966 right eyes) from 2282 patients (1283 women, 999 men), and labeled 8 types of lesions, laser scars, drusen, cup disc ratio ($$C/D>0.6$$ C / D > 0.6 ), hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates. We constructed a specialized corpus of the related fundus lesions. A multi-label classification algorithm for fundus images was proposed based on the corpus, and the collected data were trained. Results The average overall F1 Score (OF1) and the average per-class F1 Score (CF1) of the model were 0.808 and 0.792 respectively. The area under the ROC curve (AUC) of our proposed model reached 0.986, 0.954, 0.946, 0.957, 0.952, 0.889, 0.937 and 0.926 for detecting laser scars, drusen, cup disc ratio, hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates, respectively. Conclusions Our results demonstrated that our proposed model can detect a variety of lesions in the color images of the fundus, which lays a foundation for assisting doctors in diagnosis and makes it possible to carry out rapid and efficient large-scale screening of fundus lesions.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

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