PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications.
Comput Biol Med
; 108: 85-92, 2019 05.
Article
in En
| MEDLINE
| ID: mdl-31003183
ABSTRACT
In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the proposed architecture is validated with a novel hardware model for detecting Chronic Kidney Disease (CKD) from saliva samples. For detecting CKD, the urea concentration in the saliva sample is monitored by converting it into ammonia. The urea on hydrolysis in the presence of urease enzyme produces ammonia. This ammonia is then measured using a semiconductor gas sensor. The sensor response is given to the proposed architecture for feature extraction and classification. The performance of the architecture is optimized by regulating the parameter values using a PSO algorithm. The proposed architecture outperforms current conventional methods, as this approach is a combination of strong feature extraction and classification techniques. Optimal features are extracted directly from the raw signal, aiming to reduce the computational time and complexity. The proposed architecture has achieved an accuracy of 98.25%.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Neural Networks, Computer
/
Support Vector Machine
/
Models, Theoretical
Type of study:
Diagnostic_studies
Language:
En
Journal:
Comput Biol Med
Year:
2019
Document type:
Article
Affiliation country:
India