Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm. (September 2022)

• Record Type: Journal Article
• Authors: Tandel, Gopal S.; Tiwari, Ashish; Kakde, O.G.
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Ensemble -- Majority voting -- Magnetic resonance imaging -- Brain tumor -- Deep learning -- Classification -- Transfer learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104018
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Global idea of brain tumor grading system. Highlights: MRI-based computer-aided diagnosis tool is proposed for automated glioma classification into low-grade and high-grade. Classification performance of segmentation methods such as region of interest and skull-stripping are compared with whole-brain MRI (non-segmentation) data. To maximize the performance of five convolutional neural networks, a majority voting-based ensemble algorithm is proposed. Analyze the effect of the deep layers on accuracy and training time. Abstract: Glioma is the most common brain tumor in humans. Accurate stage estimation of the tumor is essential for treatment planning. The biopsy is the gold standard method for this purpose. However, it is an invasive procedure, which can prove fatal for patients, if a tumor is present deep inside the brain. Therefore, a magnetic resonance imaging (MRI) based non-invasive method is proposed in this paper for low-grade glioma (LGG) versus high-grade glioma (HGG) classification. To maximize the above classification performance, five pre-trained convolutional neural networks (CNNs) such as AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 are assembled using a majority voting mechanism. Segmentation methods require human intervention and additional computational efforts. It makes computer-aided diagnosis tools semi-automated. To analyze the performance effect of segmentation methods, three segmentation methods such as region of interest MRI segmentation (RSM) and skull-stripped MRI segmentation (SSM), and whole-brain MRI (WBM) (non-segmentation) data were compared using above mentioned algorithm. The highest classification accuracy of 99.06 ± 0.55 % was observed on the RSM data and the lowest accuracy of 98.43 ± 0.89 % was observed on the WSM data. However, only a 0.63 % improvement was found in the accuracy of the RSM data against the WBM data. This shows that deep learning models have an incredible ability to extract appropriate features from images. Furthermore, the proposed algorithm showed 2.85 %, 1.39 %, 1.26 %, 2.66 %, and 2.33 % improvement in the average accuracy of the above three datasets over the AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models, respectively.