scholarly journals Deep Learning Type Convolution Neural Network Architecture for Multiclass Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Network Architecture ◽  
Multiclass Classification ◽  
Convolution Neural Network ◽  
Neural Network Architecture

scholarly journals Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

2021 ◽  
Vol 8 (1) ◽  
pp. 25-32
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P. ◽  
M. Kusuma
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Architecture ◽  
Network Architectures ◽  
Neural Network Architecture ◽  
Brain Disorder ◽  
Different Types ◽  
Promising Treatment ◽  
Diagnostic Symptoms

Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.

scholarly journals Design of Secure Blockchain Convolution Neural Network Architecture for Detection Malware Attacks

2020 ◽  
Vol 8 (6) ◽  
pp. 3055-3060
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Information Privacy ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Neural Network Architecture ◽  
Network Method ◽  
Advance Method ◽  
Learning Concept

Nowadays, Cyberattack continues to target the applications and networks more than past with different and advance ways like programming complex format of malware that it executes unauthorized action on the targeted system, so it is needed to develop and deploy advance method to these kind of attacks for detecting correctly with a trusted and a better accuracy. Therefore, the recent solutions to detect malware attacks focuses on new advance technologies like Deep learning and Machine learning concepts. In this paper we have developed secure blockchain convolution (SBC) Algorithm that provides a better way of analyzing malware data with effectiveness and accuracy. The deep learning concept does not involve in a method to identify the trust while the process is led to extraction of the features as it can be infected by the intervention of human or a trained system. Therefore, According to research which is done towards blockchain, it features as authentication function, immutable property, information privacy and safety helps in deployment of Convolution Neural Network method with better detection. Blockchain has a decentralized structure which is able to record the data between various parties and it helps in preventing the manipulation when the deep learning concept is applied and the higher detection accuracy is received in the limited time.

Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease

2020 ◽  
pp. 1358-1382
Author(s):  
Rekh Ram Janghel
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Magnetic Resonance Image ◽  
Cross Validation ◽  
Convolution Neural Network ◽  
Learning Networks ◽  
Mri Scans

Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet, and R-CNN architecture. The best accuracies for 75-25 cross validation and 90-10 cross validation are 97.68% and 98.75%, respectively, and achieved by ZFNet architecture of convolution neural network. This research will help in further studies on improving the accuracy of Alzheimer's diagnosis and prediction using neural networks.

Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease

2018 ◽  
pp. 193-217
Author(s):  
Rekh Ram Janghel
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Magnetic Resonance Image ◽  
Cross Validation ◽  
Convolution Neural Network ◽  
Learning Networks ◽  
Mri Scans

Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet, and R-CNN architecture. The best accuracies for 75-25 cross validation and 90-10 cross validation are 97.68% and 98.75%, respectively, and achieved by ZFNet architecture of convolution neural network. This research will help in further studies on improving the accuracy of Alzheimer's diagnosis and prediction using neural networks.

scholarly journals Detection and Classification of Potato Diseases Potato Using a New Convolution Neural Network Architecture

2021 ◽  
Vol 38 (6) ◽  
pp. 1783-1791
Author(s):  
Ali Arshaghi ◽  
Mohsen Ashourin ◽  
Leila Ghabeli
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Agricultural Products ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Defect Classification ◽  
Neural Network Architecture ◽  
Surface Image ◽  
Non Destructive

Using machine vision and image processing as a non-destructive and rapid method can play an important role in examining defects of agricultural products, especially potatoes. In this paper, we propose a convolution neural network (CNN) to classify the diseased potato into five classes based on their surface image. We trained and tested the developed CNN using a database of 5000 potato images. We compared the results of potato defect classification based on CNN with the traditional neural network and Support Vector Machine (SVM). The results show that the accuracy of the deep learning method is higher than the Traditional Method. We get 100% and 99% accuracy in some of the classes, respectively.

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