Enhancing the Identification of Brain Tumours Using the CNN Ensemble Model

Abstract

The early and accurate detection of brain tumors is a critical challenge in diagnostics and healthcare due to the severe consequences of delayed diagnosis. This paper addresses this issue by employing an ensemble of Convolutional Neural Network (CNN) models to enhance the identification of brain tumors using MRI images. The methodology integrates pre-processing techniques such as image augmentation, Gaussian blurring, and Sobel edge detection to improve image quality. Various CNN architectures, including Scratch CNN, InceptionV3, Xception, EfficientNetB0, ResNet50, and VGG19, were evaluated alongside machine learning classifiers such as AdaBoost, Random Forest, SVM, KNN, and SoftMax. Among these, EfficientNetB0, Xception, and InceptionV3 demonstrated superior performance, achieving the highest classification accuracy of 98.67% and an average accuracy of 96.90%. This research underscores the significance of selecting appropriate models and classifiers for medical image classification and highlights the potential for further advancements in clinical applications.