Open AccessA Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques
Author(s) -
Prashantha Sj,
H. N. Prakash
Publication year - 2021
Publication title -
international journal of online and biomedical engineering
Language(s) - English
Resource type - Journals
ISSN - 2626-8493
DOI - 10.3991/ijoe.v17i11.25193
Subject(s) - artificial intelligence , pattern recognition (psychology) , convolutional neural network , computer science , deep learning , support vector machine , feature extraction , classifier (uml) , robustness (evolution) , merge (version control) , machine learning , biochemistry , chemistry , information retrieval , gene
Nowadays, Deep learning (DL) is the growing trend towards creating visual representations of human body organs for clinical analysis, medical interventions as well as to diagnose and treat diseases. This paper propose a method for neonatal and pediatric brain tumors image analysis and prerequisites a T2- weighted MR images only. The pipeline stages of the proposed work as follows: In the first stage, designed a set of specific feature vectors description for high-level classification task using Conventional and deep learning (DL) Feature Extraction methods. The second stage, select a deep features based on proposed convolutional neural network (CNN) method and conventional subset features are from Genetic Algorithm (GA). The third stage, merge the selected features by adapting fusion technique. Finally, predict the brain image is either normal or abnormal. The results demonstrated that the proposed method obtained accurate classification and revealed its robustness to difference in ages and acquisition protocols. The obtained results shows that based on combined deep learning features (DLF) and conventional features have been significantly improves the classification accuracy of the support vector machines (SVM) classifier up to 97.00%.