Dual Learning Model for Multiclass Brain Tumor Classification

Author First name, Last name, Institution

Rohit Thanki
Sanaa Kaddoura, Zayed UniversityFollow

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

10.1007/978-3-031-06746-4_34

ISSN

2367-3389

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

85131930108

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