Abstract
Presently, cognitive Internet of Things (CIoT) with cloud computing (CC) enabled intelligent healthcare models are developed, which enables communication with intelligent devices, sensor modules, and other stakeholders in the healthcare sector to avail effective decision making. On the other hand, Alzheimer disease (AD) is an advanced and degenerative illness which injures the brain cells, and its earlier detection is necessary for suitable interference by healthcare professional. In this aspect, this paper presents a new Oriented Features from Accelerated Segment Test (FAST) with Rotated Binary Robust Independent Elementary Features (BRIEF) Detector (ORB) with optimal artificial neural network (ORB-OANN) model for AD diagnosis and classification on the CIoT based smart healthcare system. For initial pre-processing, bilateral filtering (BLF) based noise removal and region of interest (RoI) detection processes are carried out. In addition, the ORB-OANN model includes ORB based feature extractor and principal component analysis (PCA) based feature selector. Moreover, artificial neural network (ANN) model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm (SSA). A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.
Highlights
During this digital age of modern technology, the state-of-art growths from Internet of Things (IoT) in 5G telecommunication networks, an artificial intelligence (AI) which contains Machine Learning (ML) techniques (Extreme Learning Machine (ELM), Reinforcement learning (RL), Random Forest (RF), Convolution Neural Networks (CNN), Decision Tree (DT), Long Short-term Memory Network (LSTM), Naive Bayes (NB), etc.) and deep learning (DL) approaches giving longterm solution for tackling the COVID-19 epidemic [1,2]
This paper presents a new Oriented Features from Accelerated Segment Test (FAST) with Rotated Binary Robust Independent Elementary Features (BRIEF) Detector (ORB) with optimal artificial neural network (ORB-OANN) for Alzheimer disease (AD) diagnosis and classification on the cognitive IoT (CIoT) based smart healthcare system
The data acquisition process takes place using smart computed tomography (CT) scanners. They are linked into the network and each scanner acts as an individual node
Summary
During this digital age of modern technology, the state-of-art growths from Internet of Things (IoT) in 5G telecommunication networks, an artificial intelligence (AI) which contains Machine Learning (ML) techniques (Extreme Learning Machine (ELM), Reinforcement learning (RL), Random Forest (RF), Convolution Neural Networks (CNN), Decision Tree (DT), Long Short-term Memory Network (LSTM), Naive Bayes (NB), etc.) and deep learning (DL) approaches giving longterm solution for tackling the COVID-19 epidemic [1,2] This technology was used for improving the analysis and cure along with help in the prevention of extent of this disease. This paper presents a new Oriented Features from Accelerated Segment Test (FAST) with Rotated Binary Robust Independent Elementary Features (BRIEF) Detector (ORB) with optimal artificial neural network (ORB-OANN) for AD diagnosis and classification on the CIoT based smart healthcare system.
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