scholarly journals A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

2019 ◽  
Vol 8 (2S4) ◽  
pp. 979-985
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Tumor Detection ◽  
Deep Belief Network ◽  
Support Vector ◽  
Mr Images ◽  
Belief Network ◽  
Early Tumor ◽  
Precision Rate

Early tumor detection in brain plays vital role in early tumor detection and radiotherapy. MR images are used as the input image for brain tumor finding and classify the type of brain tumor. For early detection or prediction of the brain tumor, an improved feature extraction technique along with Deep Neural Network (DNN) has been recommended. First, MR image is pre-processed, segmented and classified utilizing image processing techniques. Support Vector Machine (SVM) based brain tumor classifications are achieved previously with less precision rate. By integrating DCNN(Deep Convolutional Neural Network) classifier and DBN(Deep Belief Network), an improvement in precision rate can be achieved. This paper mainly focuses on six features viz., entropy, mean, correlation, contrast, energy and homogeneity. The proposed method is used to identify the place, locality and dimension (size) of the tumor in the cerebrum through MR copy using MATLAB software. The performance metrics recall, precision, sensitivity, accuracy and specificity are achieved.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wentao Wu ◽  
Daning Li ◽  
Jiaoyang Du ◽  
Xiangyu Gao ◽  
Wen Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Segmentation ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

scholarly journals Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8498
Author(s):  
Lei Yang ◽  
Chunqing Zhao ◽  
Chao Lu ◽  
Lianzhen Wei ◽  
Jianwei Gong
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Front Wheel ◽  
Driving Behavior ◽  
Deep Belief Network ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Safe Driving ◽  
Belief Network

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

scholarly journals Fault Signal Recognition in Power Distribution System using Deep Belief Network

2018 ◽  
Vol 29 (1) ◽  
pp. 459-474
Author(s):  
T.C. Srinivasa Rao ◽  
S.S. Tulasi Ram ◽  
J.B.V. Subrahmanyam
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Power Distribution ◽  
Distribution System ◽  
Electrical Power ◽  
Deep Belief Network ◽  
Support Vector ◽  
Neural Network Models ◽  
Power Distribution System ◽  
Belief Network

Abstract Nowadays, electrical power system is considered as one of the most complicated artificial systems all over the globe, as social and economic development depends on intact, consistent, stable and economic functions. Owing to diverse random causes, accidental failures occur in electrical power systems. Considering this issue, this article aimed to propose the use of deep belief network (DBN) in detecting and classifying fault signals such as transient, sag and swell in the transmission line. Here, wavelet-decomposed fault signals are extracted and the fault is diagnosed based on the decomposed signal by the DBN model. Further, this article provides the performance analysis by determining the types I and II measures and root-mean-square-error (RMSE) measure. In the performance analysis, it compares the performance of the DBN model to various conventional models like linear support vector machine (SVM), quadratic SVM, radial basis function SVM, polynomial SVM, multilayer perceptron SVM, Levenberg-Marquardt neural network and gradient descent neural network models. The simulation results validate that the proposed DBN model effectively detects and classifies the fault signal in power distribution system when compared to the traditional model.

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