scholarly journals Development of a Novel Neural Network Model for Brain Image Classification

2019 ◽  
Vol 8 (3) ◽  
pp. 7230-7235 ◽  
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Brain Mri ◽  
Classification Performance ◽  
Feature Reduction ◽  
Machine Learning Techniques ◽  
Mr Image ◽  
Medical Practitioners ◽  
Learning Techniques ◽  
The Brain

The analysis of brain MRI images is highly beneficial for the medical practitioners. Since the manual study of these images are time consuming and tedious, the automated process using software based system have been developed. The machine learning techniques are applied in developing brain MR image classification process. The classification process consists of dataset preparation, feature extraction, feature reduction and the use of classifier. In this paper, 2D DWT is used for feature extraction and PCA is used for feature reduction. ELM model is used as a classifier. The input weights and biases in ELM are randomly assigned. So EHO algorithm, a newly developed bio inspired algorithm is used to optimally determine the input weights and biases of ELM model. The classification performance of the EHO-ELM model is compared with basic ELM model for three of the brain MR image datasets. From the simulation study, it is found that the proposed EHO-ELM model outperformed the basic ELM model.

scholarly journals Alzheimer's Disease: A Survey

2021 ◽  
Vol 8 (1) ◽  
pp. 33-39
Author(s):  
Harshitha ◽  
Gowthami Chamarajan ◽  
Charishma Y
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Neural Networks ◽  
Brain Area ◽  
Brain Mri ◽  
Three Dimensional ◽  
The Other ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Brain

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

scholarly journals Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases

2021 ◽  
Vol 23 ◽  
pp. 100545
Author(s):  
Israel Elujide ◽  
Stephen G. Fashoto ◽  
Bunmi Fashoto ◽  
Elliot Mbunge ◽  
Sakinat O. Folorunso ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychotic Disorder ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques

Prediction of Neurological Disorders Using Visual Saliency

2021 ◽  
pp. 1-11
Author(s):  
Sreelakshmi S. ◽  
Anoop V. S.
Keyword(s):  
Neurological Disorders ◽  
Early Stage ◽  
Brain Magnetic Resonance Imaging ◽  
Visual Saliency ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri ◽  
Processing Techniques ◽  
The Brain ◽  
Prompt Diagnosis

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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.

Feature Reduction and Optimization of Malware Detection System Using Ant Colony Optimization and Rough Sets

2020 ◽  
Vol 14 (3) ◽  
pp. 95-114
Author(s):  
Ravi Kiran Varma Penmatsa ◽  
Akhila Kalidindi ◽  
S. Kumar Reddy Mallidi
Keyword(s):  
Ant Colony Optimization ◽  
Detection System ◽  
Malware Detection ◽  
Feature Reduction ◽  
Ant Colony ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Feature Significance ◽  
Malware Detection And Classification ◽  
Malicious Program

Malware is a malicious program that can cause a security breach of a system. Malware detection and classification is one of the burning topics of research in information security. Executable files are the major source of input for static malware detection. Machine learning techniques are very efficient in behavioral-based malware detection and need a dataset of malware with different features. In windows, malware can be detected by analyzing the portable executable (PE) files. This work contributes to identifying the minimum feature set for malware detection employing a rough set dependent feature significance combined with Ant Colony Optimization (ACO) as the heuristic-search technique. A malware dataset named claMP with both integrated features and raw features was considered as the benchmark dataset for this work. The analytical results prove that 97.15% and 92.8% data size optimization has been achieved with a minimum loss of accuracy for claMP integrated and raw datasets, respectively.

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