Dual Learning Model for Multiclass Brain Tumor Classification
Document Type
Book Chapter
Source of Publication
Lecture Notes in Networks and Systems
Publication Date
5-27-2022
Abstract
A brain tumor occurs in the human body when the brain develops abnormal cells. Tumors are called either benign (noncancerous) or malignant (cancerous). The function of the nervous system is affected by the growth rate and the location of the tumor. The tumor treatment depends on tumor type, size, and location. Artificial intelligence has been widely used to automatically predict various brain tumors using multiple imaging technologies such as magnetic resonance imaging (MRI) and computerized tomography (CT) scan during the last few years. This paper applies a hybrid learning based classifier on an MRI dataset containing benign and malignant images. Moreover, deep learning is also applied to the same dataset. The proposed learning approach’s performance is compared to other existing supervised machine learning approaches. The experimental results show that our proposed approach outperforms the existing approaches available in the literature.
DOI Link
ISSN
Publisher
Springer International Publishing
Volume
484
First Page
350
Last Page
360
Disciplines
Computer Sciences | Medicine and Health Sciences
Keywords
Brain tumor, Hybrid machine learning, Deep learning, Magnetic resonance imaging, Malignant
Scopus ID
Recommended Citation
Thanki, Rohit and Kaddoura, Sanaa, "Dual Learning Model for Multiclass Brain Tumor Classification" (2022). All Works. 5153.
https://zuscholars.zu.ac.ae/works/5153
Indexed in Scopus
yes
Open Access
no