Small sample color fundus image quality assessment based on gcforest

Lv, Hao; Zhang, Ning; Jin, Shangang; Xu, Dezhi; Gao, Weizhe

doi:10.1007/s11042-020-09362-y

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…But the results are not appreciable with deep learning networks due to limitations with less training size. Liu et al 16 employed the gcforest classifier, which comprises a set of RFs in their approach, but due to the variation in the training data size, every test produces different results.…”

Section: Resultsmentioning

confidence: 99%

“…Gour et al 15 proposed their system with Gabor filter‐based (GIST) features and pyramid histogram oriented gradient (PHOG) features and classified their system using SVM by implementing it on high‐resolution fundus (HRF) and Drishti‐GS1 datasets. Liu et al 16 developed a DNN model by extracting texture and color features and implemented it on several datasets. An 18‐layer CNN model was designed by Elangovan et al 17 for the classification of glaucoma and verified on five public datasets.…”

Section: Review Of the Literaturementioning

confidence: 99%

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Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Sonti,

Dhuli

2024

Int J Imaging Syst Tech

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Deep learning is an emerging trend with enormous applications over the past few years. Ophthalmology is one such area in medical applications where early disease detection is required to avoid loss of vision. Glaucoma is a rapidly growing disorder related to human eye, which arises due to the increase in pressure inside the eye. The medical diagnosis methods available for glaucoma have some limitations; hence, computer‐aided design (CAD) approach is preferred using images. In the context of image processing, convolution neural networks (CNNs) are preferred for classification because of their ability to grasp highly discriminate features from raw pixel intensities. In our approach, diagnosis of glaucoma is implemented by extracting the region of interest (ROI) by splitting the coefficients into recurrence decays and will improve the possibility of identifying even poorly differentiated exudates and upgrading the normal recurrence ranges. Later, a sequential deep neural network (DNN) model with a rectified linear unit (ReLU) and sigmoid function is designed to train the data with effective features matching from training and testing samples. The proposed model is implemented on two publicly available datasets (Drishti‐GS1 and ACRIMA) using 10‐fold cross validation (CV), 60:40 and 70:30 split ratio approaches, and performance is assessed using the metrics and plotted the region of convergence curves. The model is also tested on two more datasets (ORIGA and Refuge) to validate the robustness of the proposed model. The obtained simulation results and the evaluated performance metrics prove that our proposed model diagnose glaucoma from retinal fundus images effectively compared with other existing models.

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Section: Resultsmentioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Sonti,

Dhuli

2024

Int J Imaging Syst Tech

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“…Molecular descriptors and molecular fingerprints of each ligand could be obtained, which contains 374 features. In order to better reflect the effectiveness of forgeNet, three classical classifiers (SVM 42 , RF 43 , logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest 44 ) are utilized to identify the compounds associated with diseases. Five evaluation criteria of classifier performance are utilized, which are SN , SP , Kappa , MCC and F 1, respectively.…”

Section: Methodsmentioning

confidence: 99%

Disease-related compound identification based on deeping learning method

Yang

Bao

Wang

et al. 2022

Sci Rep

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Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

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“…Different from the traditional Softmax classifier, the hidden features of the input feature vector can be learned by gcForest through the superposition of multi-layer random forests, which then output the final classification results [29,30]. It has been proven that the accuracy of the deep forest classifier is about 1-4% higher than that of the Softmax classifier.…”

Section: Gcforestmentioning

confidence: 99%

Bearing Fault Diagnosis Based on a Novel Adaptive ADSD-gcForest Model

2022

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With the continuous improvement of industrial production requirements, bearings work significantly under strong noise interference, which makes it difficult to extract fault features. Deep Learning-based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the structure of deep learning is often determined by trial and error, which is time-consuming and lacks theoretical support. To address the above problems, an adaptive (Adaptive Depthwise Separable Dilated Convolution and multi-grained cascade forest) ADSD-gcForest fault diagnosis model is proposed in this paper. Multiscale convolution combined with convolutional attention mechanism (CBAM) concentrates on effectively extracting fault information under strong noise, and the Meta-Activate or Not (Meta-ACON) activation function is integrated to adaptively optimize the model structure according to the characteristics of input samples, then gcForest outputs the final diagnosis result as the classifier. The experiment compares the effects of three bearings failure diagnoses under various noise and load conditions. The experimental results show the effectiveness and practicability of the proposed method.

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Small sample color fundus image quality assessment based on gcforest

Cited by 10 publications

References 35 publications

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Disease-related compound identification based on deeping learning method

Bearing Fault Diagnosis Based on a Novel Adaptive ADSD-gcForest Model

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