Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification

Gopal S. Tandel

doi:10.1016/j.compbiomed.2021.104564

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Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification

2021-08-01

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Gopal S. Tandel *

Ashish Tiwari

O. G. Kakde

DOI：10.1016/j.compbiomed.2021.104564

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Abstract

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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 DLbased models (convolutional neural networks), AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, and five ML-based models, Support Vector Machine, K-Nearest Neighbours, Naive 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.

Keywords:

Ensemble

Majority voting

Convolutional neural network

Machine learning

Deep learning

Transfer learning

Magnetic resonance imaging

Computer-aided diagnosis