scholarly journals Deep Learning Provisions in the Matlab: Focus on CNN Facility

2019 ◽  
Vol 8 (9S3) ◽  
pp. 990-994
Keyword(s):  
Computer Tomography ◽  
Classification Accuracy ◽  
Lung Diseases ◽  
Back Propagation ◽  
Normal Lung ◽  
Complex Wavelet Transform ◽  
Segmented Image ◽  
Entire Lung ◽  
Propagation Network

Currently, Lung diseases are the major problem that affect the lungs which is an important the organs that allow us to survive through breathing. The diseases such as pleural effusion, Asthma, chronic bronchitis, and normal lung are detected and classified in this work. This paper presents a Computer Tomography (CT) Images of lungs for detection of diseases which is developed using ANN-BPN. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform and GLCM Features. The entire lung is segmented from the Computer Tomography Images and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the ANN-Back Propagation Network designed for classification of ILD patterns. The parameters gives the maximum classification Accuracy. After result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.

scholarly journals Lung Pattern Classification for Interstitial Lung Diseases using ANN-Back Propagation Network

2019 ◽  
Vol 8 (4) ◽  
pp. 3344-3349
Keyword(s):  
Wavelet Transform ◽  
Lung Diseases ◽  
Back Propagation ◽  
Ct Images ◽  
Interstitial Lung Diseases ◽  
Complex Wavelet Transform ◽  
Entire Lung ◽  
Lung Infections ◽  
Component Extraction ◽  
Propagation Network

Lung infections are the messes that influence the lungs, the organs that empower us to breathe in and it is the most notable illnesses worldwide especially in India. The sicknesses, for instance, pleural radiation and customary lung are distinguished and masterminded in this work. This paper presents a PC helped gathering Method in Computer Tomography (CT) Images of lungs made using ANN-BPN. The inspiration driving the work is to distinguish and arrange the lung illnesses by compelling component extraction through Dual-Tree Complex Wavelet Transform and GLCM Features. The entire lung is assigned from the CT Images and the parameters are resolved from the separated picture. The parameters are resolved using GLCM. We Propose and survey the ANN-Back Propagation Network planned for gathering of ILD structures

scholarly journals A neural network-based electromyography motion classifier for upper limb activities

2016 ◽  
Vol 09 (06) ◽  
pp. 1650025 ◽  
Author(s):  
Karan Veer ◽  
Tanu Sharma ◽  
Ravinder Agarwal
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Cross Validation ◽  
Classification Scheme ◽  
Back Propagation ◽  
Recognition Method ◽  
Neural Network Classification ◽  
Neural Classifier ◽  
Propagation Network

The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG) pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC). Further, the realization of projected network was verified using cross validation (CV) process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE) value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA) of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition tasks.

scholarly journals Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network 

2014 ◽  
Vol 32 (No. 3) ◽  
pp. 280-287 ◽  
Author(s):  
I. Golpour ◽  
ParianJA ◽  
R.A. Chayjan
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Rice Cultivars ◽  
Image Processing Algorithm ◽  
White Rice ◽  
Colour Features

We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

scholarly journals Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

2019 ◽  
Vol 8 (4) ◽  
pp. 6654-6659
Keyword(s):  
Feature Extraction ◽  
Spectral Analysis ◽  
Power Quality ◽  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Renewable Energy Resources ◽  
Statistical Parameters ◽  
Singular Spectral Analysis

In real power system, Power quality disturbances (PQDs) have become major challenge due to the introduction of renewable energy resources and embedded power systems. In this research, two novel feature extraction methods multi resolution analysis wavelet transform (MRA-WT) and Multiscale singular spectral analysis (MSSA) have been analysed with convolution neural network classifier for the classification of PQDs. Statistical parameters are also applied for the optimal feature selection. MSSA is time-series tool and MRA-WT are applied for feature extraction and 1-dimensional CNN (1-DCNN) is used to classify the single and multiple PQDs. The architecture is built with forward propagation and back propagation is utilized to tune the data. Finally, the results of two selected feature extraction techniques are compared with classification accuracy. The simulation based results explained that MSSA with 1-DCNN has significantly higher classification accuracy under different noisy conditions.

scholarly journals Chest X-Ray Classification of Lung Diseases Using Deep Learning

2021 ◽  
Vol 1 (1) ◽  
pp. 12-18
Author(s):  
Yew Fai Cheah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Viral Pneumonia ◽  
Normal Lung ◽  
X Ray ◽  
Chest X Ray ◽  
Online Sources

Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. The classification task is separated into three and four classes, with COVID-19, normal, TB, and viral pneumonia, while the three-class problem excludes the normal lung. During testing, AlexNet and ResNet-18 gave promising results, scoring more than 95% accuracy.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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