A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques

<p class="0abstract">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 tu...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Prashantha SJ, H.N. Prakash
Formato: article
Lenguaje:EN
Publicado: International Association of Online Engineering (IAOE) 2021
Materias:
Acceso en línea:https://doaj.org/article/9792fc8a42da4726b8a85364b39097df
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:<p class="0abstract">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%.</p>