scholarly journals Classification of Diabetic Retinopathy using Stacked Autoencoder-Based Deep Neural Network

2020 ◽  
Vol 1 (1) ◽  
pp. 9-14
Author(s):  
Yasir Eltigani Ali Mustaf ◽  
◽  
Bashir Hassan Ismail ◽  
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Data Set ◽  
Learning Framework ◽  
Training Images ◽  
Stacked Autoencoder

Diagnosis of diabetic retinopathy (DR) via images of colour fundus requires experienced clinicians to determine the presence and importance of a large number of small characteristics. This work proposes and named Adapted Stacked Auto Encoder (ASAE-DNN) a novel deep learning framework for diabetic retinopathy (DR), three hidden layers have been used to extract features and classify them then use a Softmax classification. The models proposed are checked on Messidor's data set, including 800 training images and 150 test images. Exactness, accuracy, time, recall and calculation are assessed for the outcomes of the proposed models. The results of these studies show that the model ASAE-DNN was 97% accurate.

scholarly journals A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification

2020 ◽  
Vol 11 (2) ◽  
pp. 44-57
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Texture Features ◽  
Classification Performance ◽  
Learning Approaches ◽  
Learning Framework ◽  
Classification Framework ◽  
Feature Based

The classification of plants is one of the most important aims for botanists since plants have a significant part in the natural life cycle. In this work, a leaf-based automatic plant classification framework is investigated. The aim is to compare two different deep learning approaches named Deep Neural Network (DNN) and deep Convolutional Neural Network (CNN). In the case of deep neural network, hybrid shapes and texture features are utilized as hand-crafted features while in the case of the convolution non-handcraft, features are applied for classification. The offered frameworks are evaluated with a public leaf database. From the simulation results, it is confirmed that the deep CNN-based deep learning framework demonstrates superior classification performance than the handcraft feature based approach.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

A Similarity-Based Object Classification Using Deep Neural Networks

2019 ◽  
pp. 197-219
Author(s):  
Parvathi R. ◽  
Pattabiraman V.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Random Projection ◽  
Learning Networks ◽  
Learning Models ◽  
External Conditions ◽  
And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

scholarly journals Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Daniel G. E. Thiem ◽  
Paul Römer ◽  
Matthias Gielisch ◽  
Bilal Al-Nawas ◽  
Martin Schlüter ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Oral Mucosa ◽  
Deep Neural Network ◽  
Light Weight ◽  
Tissue Classification ◽  
Raw Data

Abstract Background Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. Methods A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). Results The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. Conclusion Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

scholarly journals Automated Detection and Classification of Diabetic Retinopathy using ConvNets

2021 ◽  
Vol 9 (10) ◽  
pp. 1360-1369
Author(s):  
Aavani B
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Confusion Matrix ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Data Set ◽  
Permanent Loss ◽  
Process Computer ◽  
Degree Classification

Abstract: Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network model is trained by using this fundus image and five-degree classification task is performed. We were able to produce an sensitivity of 90%. Keywords: Confusion matrix, Deep convolutional Neural Network, Diabetic Retinopathy, Fundus image, OCT

scholarly journals An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Umashankar Subramaniam ◽  
M. Monica Subashini ◽  
Dhafer Almakhles ◽  
Alagar Karthick ◽  
S. Manoharan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Left Lung ◽  
X Ray ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Accuracy Of Results

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

scholarly journals A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening

2021 ◽  
Author(s):  
Yuanyuan Jiang ◽  
Jiali Guo ◽  
Yjing Liu ◽  
Yanzhi Guo ◽  
Menglong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Molecular Descriptors ◽  
Molecular Graph ◽  
Feature Space ◽  
Reliable Data ◽  
Feature Representation ◽  
Data Set ◽  
Learning Framework ◽  
Priori Knowledge

<p>Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization.</p><br>

scholarly journals A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

2020 ◽  
Vol 34 (6) ◽  
pp. 683-692
Author(s):  
Shaik Akbar ◽  
Divya Midhunchakkaravarthy
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Neural Network ◽  
Feature Space ◽  
True Positive Rate ◽  
Vital Role ◽  
Learning Framework ◽  
Segmentation Approach ◽  
Positive Rate ◽  
Conventional Models

Image thresholding-based segmentation models play a vital role in the detection of Diabetic retinopathy (DR) on large databases. Most of the conventional segmentation-based classification models are independent of over segmented regions and outliers. Also, these models have less true positive rate and high error rate on different DR feature sets. In order to overcome these problems, a novel filtered based segmentation framework is designed and implemented on the large DR feature space. In this work, a novel image filtering approach, optimal image segmentation approach and hybrid Bayesian deep learning framework are developed on the large DR image databases. Experimental results proved that the proposed filtered segmentation-based Bayesian deep neural network has better accuracy and runtime than the conventional models on different DR variation databases.

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