Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification.
Comput Biol Med
; 135: 104564, 2021 08.
Article
in En
| MEDLINE
| ID: mdl-34217980
ABSTRACT
BACKGROUND:
Although biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-based non-invasive brain tumour grading method has been proposed using deep learning (DL) and machine learning (ML) techniques.METHOD:
Four clinically applicable datasets were designed. The four datasets were trained and tested on five DL-based models (convolutional neural networks), AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, and five ML-based models, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, Decision Tree, and Linear Discrimination using five-fold cross-validation. A majority voting (MajVot)-based ensemble algorithm has been proposed to optimise the overall classification performance of five DL and five ML-based models.RESULTS:
The average accuracy improvement of four datasets using the DL-based MajVot algorithm against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models was 2.02%, 1.11%, 1.04%, 2.67%, and 1.65%, respectively. Further, a 10.12% improvement was seen in the average accuracy of four datasets using the DL method against ML. Furthermore, the proposed DL-based MajVot algorithm was validated on synthetic face data and improved the male versus female face image classification accuracy by 2.88%, 0.71%, 1.90%, 2.24%, and 0.35% against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, respectively.CONCLUSION:
The proposed MajVot algorithm achieved promising results for brain tumour classification and is able to utilise the combined potential of multiple models.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain Neoplasms
/
Deep Learning
Type of study:
Prognostic_studies
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Comput Biol Med
Year:
2021
Document type:
Article