scholarly journals Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Mateen ◽  
Junhao Wen ◽  
Nasrullah Nasrullah ◽  
Song Sun ◽  
Shaukat Hayat
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Region Of Interest ◽  
The Other ◽  
Previous Knowledge ◽  
Residual Network ◽  
Softmax Classifier ◽  
Fully Connected ◽  
Visual Geometry

In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates.

scholarly journals Diabetic Retinopathy Severity Detection using Convolutional Neural Network

2020 ◽  
Vol 32 ◽  
pp. 01012
Author(s):  
Mayank Shete ◽  
Saahil Sabnis ◽  
Srijan Rai ◽  
Gajanan Birajdar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Feature Learning ◽  
Eye Diseases ◽  
Accuracy Rate ◽  
Irreversible Damage ◽  
Computer Aided

Diabetic Retinopathy is one of the most prominent eye diseases and is the leading cause of blindness amongst adults. Automatic detection of Diabetic Retinopathy is important to prevent irreversible damage to the eye-sight. Existing feature learning methods have a lesser accuracy rate in computer aided diagnostics; this paper proposes a method to further increase the accuracy. Machine learning can be used effectively for the diagnosis of this disease. CNN and transfer learning are used for the severity classification and have achieved an accuracy of 73.9 percent. The use of XGBoost classifier yielded an accuracy of 76.5 percent.

scholarly journals Convolutional recurrent neural network with template based representation for complex question answering

2020 ◽  
Vol 10 (3) ◽  
pp. 2710
Author(s):  
A. Chandra Obula Reddy ◽  
K. Madhavi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Internal Representation ◽  
Semantic Matching ◽  
Softmax Classifier ◽  
Questions And Answers ◽  
Fully Connected ◽  
The Given ◽  
Complex Question

Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

DIABETIC RETINOPATHY DIAGNOSIS USING KERNEL FUZZY C MEANS WITH CONVOLUTIONAL NEURAL NETWORK BASED RESIDUAL MODEL

2021 ◽  
Vol 10 (1) ◽  
pp. 413-422
Author(s):  
K. K. Yazhini ◽  
D. Loganathan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Acquisition ◽  
Superior Performance ◽  
Residual Network ◽  
Fuzzy C Means ◽  
Initial Stage ◽  
Proposed Model ◽  
Retinal Fundus Image

Presently, Internet of Things (IoT) becomes popular owing to diverse its application scenarios like transports, building, healthcare, etc. This study introduces an efficient IoT based diabetic retinopathy (DR) diagnosis model using Kernel Fuzzy C Means Segmentation and Residual Network. The proposed model involves a sequence of processes namely image acquisition, pre-processing, segmentation, feature extraction and classification. At the initial stage, retinal fundus image acquisition takes place which captures the retina image of the patient using head mounted camera. Next, kernel fuzzy c-means (KFCM) based segmentation process is applied to identify the diseased area. Then, the features are extracted using convolutional neural network (CNN) based residual network (ResNet) model. Finally, softmax function is employed to carry out the classification task. The validation of the presented model takes place using Kaggle DR dataset and the experimental results verified the superior performance of the presented model. The obtained results indicated that the KFCM-CNNR model has resulted to a maximum accuracy of 96.89%, sensitivity of 93.12% and specificity of 98.16%.

scholarly journals Using Transfer Learning for Diabetic Retinopathy Stages Classification

2021 ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
The Body ◽  
Patients At Risk ◽  
Retinal Fundus ◽  
Automated Methods ◽  
Disease Grade ◽  
Visual Geometry

Abstract Purpose – Diabetes is a chronic disease, that leads to damage of many systems of the body. One of the dangerous complications of diabetes is diabetic retinopathy. Frequent inspection for diabetic retinopathy is essential to recognize patients at risk of visual impairment. The disease grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of diabetic retinopathy and the classification of its severity stage are necessary. It also helps to decrease the burden on ophthalmologists and reduce diagnostic contradictions among manual readers. Methods– In this research, a convolutional neural network (CNN) is used based on color retinal fundus images for the detection of diabetic retinopathy (DR) and classification of its stages. CNN can recognize sophisticated features on the retina and so provide an automatic diagnosis. The pre-trained CNN model Visual Geometry Group (VGG) is applied on DR data using a transfer learning approach to utilize the already learnt parameters based on 1,000,000 images of ImageNet with 1000 classes.Results – By conducting different experiments with different classes setting the built models achieved promising results. The best achieved accuracies for 2-ary, 3-ary, 4-ary, and 5-ary classification are 85.99, 80.5, 61.28, and 71, respectively.

scholarly journals Transfer Learning Based MA Detection (TL-MAD)

2020 ◽  
Vol 9 (3) ◽  
pp. 3653-3656
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Automatic Detection ◽  
Microvascular Complication ◽  
Fundus Images ◽  
Main Symptom ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic Retinopathy (DR) is a microvascular complication of Diabetes that can lead to blindness if it is severe. Microaneurysm (MA) is the initial and main symptom of DR. In this paper, an automatic detection of DR from retinal fundus images of publicly available dataset has been proposed using transfer learning with pre-trained model VGG16 based on Convolutional Neural Network (CNN). Our method achieves improvement in accuracy for MA detection using retinal fundus images in prediction of Diabetic Retinopathy.

scholarly journals Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 162 ◽  
Author(s):  
Jiana Meng ◽  
Yingchun Long ◽  
Yuhai Yu ◽  
Dandan Zhao ◽  
Shuang Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Target Domain ◽  
Data Set ◽  
Source Domain ◽  
Cross Domain ◽  
The Cross ◽  
Fully Connected ◽  
Text Sentiment Analysis

Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.

scholarly journals USING TRANSFER LEARNING FOR MALWARE CLASSIFICATION

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 343-349
Author(s):  
B. Prima ◽  
M. Bouhorma
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Markov Models ◽  
Superior Performance ◽  
Support Vector ◽  
Traditional Learning ◽  
Malware Classification ◽  
Classification Framework ◽  
Learning Techniques ◽  
Fully Connected

Abstract. In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.

scholarly journals Deep Learning Technology in Pathological Image Analysis of Breast Tissue

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanan Liu ◽  
Xiaoyan Wang ◽  
Jingyu Li ◽  
Liguo Hao ◽  
Tianyu Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
The Other ◽  
Cell Detection ◽  
Learning Technology ◽  
Data Set ◽  
Softmax Classifier ◽  
Window Method ◽  
Pathological Image

To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms ( P  < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant ( P  < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.

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