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.

scholarly journals An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm

2022 ◽  
Vol 12 (2) ◽  
pp. 1768
Author(s):  
Nagashree Nagesh ◽  
Premjyoti Patil ◽  
Shantakumar Patil ◽  
Mallikarjun Kokatanur
Keyword(s):  
Genetic Algorithm ◽  
Complex Structure ◽  
Autism Spectrum ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Learning Techniques ◽  
Architectural Framework ◽  
Magnetic Resonance Imaging Mri ◽  
Noninvasive Neuroimaging ◽  
The Brain

The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

Early Stage Detection of Heart Failure Using Machine Learning Techniques

2021 ◽  
pp. 75-88
Author(s):  
Zulfikar Alom ◽  
Mohammad Abdul Azim ◽  
Zeyar Aung ◽  
Matloob Khushi ◽  
Josip Car ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques

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.

A Conjunctival Vascular Malformation as a Rare Presenting Sign of Wyburn–Mason Syndrome

2017 ◽  
Vol 16 (04) ◽  
pp. 239-242
Author(s):  
Gunnar Buyse ◽  
Lieven Lagae ◽  
Philippe Demaerel ◽  
Frank Kesteloot ◽  
Ingele Casteels ◽  
...  
Keyword(s):  
Vascular Malformation ◽  
Arteriovenous Malformations ◽  
Brain Magnetic Resonance Imaging ◽  
Rare Condition ◽  
Vascular Lesion ◽  
Facial Skin ◽  
Sudden Appearance ◽  
Magnetic Resonance Imaging Mri ◽  
The Right ◽  
The Brain

AbstractWyburn–Mason syndrome is a rare condition that is characterized by ipsilateral arteriovenous malformations affecting the eye, brain, and facial skin. A conjunctival vascular dilation can be a rare ocular presenting sign. We report a 6-year-old boy who attended the hospital because of the sudden appearance of a conjunctival vascular lesion in his right eye. Inspection of his facial skin showed a subtle discoloration along the right trigeminal nerve and a vascular structure of the conjunctiva. Fundoscopy showed dilated and tortuous retinal vessels. Brain magnetic resonance imaging (MRI) revealed a large arteriovenous malformation involving the thalamus and perimesencephalic area. Ophthalmologic and neuroradiologic findings were consistent with the diagnosis of Wyburn–Mason syndrome. The sudden emergence of a vascular malformation in the conjunctiva should alert the clinician to perform an ophthalmoscopy, and in our patient, this finding was the clue to diagnosis of Wyburn–Mason syndrome. Because of the association between retinal and intracranial arteriovenous malformations, an MRI of the brain is strongly recommended in all patients with ocular arteriovenous malformations.

Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3449-3452
Author(s):  
M. S. Roobini ◽  
Y. Sai Satwick ◽  
A. Anil Kumar Reddy ◽  
M. Lakshmi ◽  
D. Deepa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
The Body ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Pima Indians ◽  
Classification Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

Automated Teller Machine Security with Image Processing and Machine Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4473-4481
Author(s):  
M. L. Chayadevi ◽  
Sujith Madhyastha ◽  
K. N. Nisarga ◽  
H. Charitha ◽  
B. Susharan
Keyword(s):  
Image Processing ◽  
Machine Learning Techniques ◽  
Alert System ◽  
Automated Teller Machines ◽  
Learning Techniques ◽  
Automated Teller Machine ◽  
Haar Cascade ◽  
Physically Challenged ◽  
Processing Techniques ◽  
Day By Day

There are many scenarios in society with thefts and crimes involved in Automated Teller Machines (ATM). These events are increasing day-by-day and which is also increasing the complexities on the crime investigation agencies. In order to deal with these situations, we have proposed an automated security method inside ATMs using image processing techniques which can alert the concerned authorities immediately whenever these types of situations arise. Hybrid method with Viola-Jones algorithm has been used for face recognition along with the Haar-cascade features. In the case of objects such as knife, gun etc. inside an ATM, combination of SVM and random forest algorithms are used for object detection. TensorFlow with machines learning algorithms have been used in the hybrid methodology. Android application has been developed to prevent and alert the crimes and send alert messages to the concerned. Speech alert system is developed to assist blind and physically challenged people.

scholarly journals Pelizaeus-Merzbacher Disease: A Case Report

2020 ◽  
Author(s):  
Ghazaleh Jamalipour Soufi ◽  
Siavash Iravan
Keyword(s):  
Case Report ◽  
White Matter ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Proteolipid Protein ◽  
Radiology Department ◽  
Magnetic Resonance Imaging Mri ◽  
Normal White Matter ◽  
Pelizaeus Merzbacher Disease ◽  
The Brain

Pelizaeus-Merzbacher Disease (PMD), as a rare genetically x-linked leukodystrophy, is a disorder of proteolipid protein expression in myelin formation. This disorder is clinically presented by neurodevelopmental delay and abnormal pendular eye movements. The responsible gene for this disorder is the proteolipid protein gene (PLP1). Our case was a oneyear-old boy referred to the radiology department for evaluating the Central Nervous System (CNS) development by brain Magnetic Resonance Imaging (MRI). Clinically, he demonstrated neuro-developmental delay symptoms. The brain MRI results indicated a diffuse lack of normal white matter myelination. This case report should be considered about the possibilityof PMD in the brain MRI of patients who present a diffuse arrest of normal white matter myelination.

scholarly journals Identification of Plant Leaf Disease using Machine Learning Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 6077-6081 ◽  
Keyword(s):  
Plant Disease ◽  
Digital Camera ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Leaf Sample ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Identification Techniques ◽  
Processing Techniques ◽  
Better Than

Plant disease identification and classification is major area of research as majority of people in India depend on agriculture for their main source of income and for food. Identification of the diseases in any crops is challenging since manual identification techniques being used in this are based on the experts advises which may not be efficient. Based on leaf features decisions about variety of diseases are taken. In this paper an automated framework is introduced which can be used to detect and classify the diseases in the leaf accurately. Leaf images are acquired by using digital camera. Pre-processing techniques, segmentation and feature extraction are performed on the acquired images. The features are passed on to the classifiers to classify the diseases. This work has been proposed to classify and distinguish the leaf sample based on its features. The proposed work is carried out with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifiers to analyze the result. For given dataset ANN performed better than the other two classifiers

scholarly journals Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

2021 ◽  
Author(s):  
Mehmet Akif Cifci
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
The Other ◽  
Machine Learning Techniques ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Fundus Photographs ◽  
Processing Techniques ◽  
Time And Energy

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

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