An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to...

Full description

Saved in:
Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2021, Vol.69 (2), p.2653-2670
Main Authors: Aziz, Ahsan, Attique, Muhammad, Tariq, Usman, Nam, Yunyoung, Nazir, Muhammad, Jeong, Chang-Won, R. Mostafa, Reham, H. Sakr, Rasha
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Brain
Brain cancer
Classification
Computer vision
Deep learning
Image analysis
Image classification
Machine learning
Medical imaging
Support vector machines
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of the tumor, selection of important features, among others. In this study, we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features. In the proposed framework, initially, a database is normalized in the form of high-grade glioma (HGG) and low-grade glioma (LGG) patients and then two pre-trained deep learning models (ResNet50 and Densenet201) are chosen. The deep learning models were modified and trained using transfer learning. Subsequently, the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models. The selected features are fused using a serial-based approach and classified using a cubic support vector machine. The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8% and 84.6% for HGG and LGG, respectively. The comparison is performed using several classification methods, and it shows the significance of our proposed technique.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2021.018606