Deep Learning-Based Investigation of Glioma Detection and Grading Using Ensemble CNN

The World Health Organization (WHO) identified Gliomas as the most widespread brain tumour category. Toxicity levels combined with growth rates serve as the basis for dividing glioma malignancies into four distinct categories. The accurate grading of tumors determines which treatment method could be best suited including chemotherapy, surgery or radiation therapy. The use of magnetic resonance imaging (MRI) images to separate and grade tumours works well yet demands significant time and money and includes intricate management variations. The objective of this study is to meet the need for precise and reliable tumour classification by proposing a computational method for glioma grading that leverages an ensemble of Convolutional Neural Networks (CNNs). The proposed method introduces two ensemble models named Mean-Ensemble-CNNs & NN-Ensemble-CNNs that merge the pretrained CNN models ResNet-101, ResNeXt-50, Inception-v3 and DenseNet-161. Neural Network Ensembles combine deep features from multiple algorithms through a neural network to identify gliomas while Mean Ensembles calculate the average predictions from various algorithms to detect gliomas. The following sections provide a detailed analysis of Mean-Ensemble-CNNs. The system utilizes advanced data preparation and augmentation methods to fully exploit available informational resources. The technique reduces dependence on invasive biopsies while enabling flexible clinical treatment planning. Automatic segmentation and grading of tumours makes this process possible. The proposed NN-Ensemble CNNs achieve the top score with 96.21% accuracy which surpasses all individual models.

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