FusionNet: Dual input feature fusion network with ensemble based filter feature selection for enhanced brain tumor classification

Abstract

Brain tumors pose a significant threat to human health, require a precise and quick diagnosis for effective treatment. However, achieving high diagnostic accuracy with traditional methods remains challenging due to the complex nature of brain tumors. Recent advances in deep learning have showed potential in automating brain tumor classification using brain MRI images, offering the potential to enhance diagnostic result. This paper present FusionNet, a novel approach that utilizing normal and segmented MRI images to achieve better classification accuracy. Segmented images are generated using a Dual Residual Blocks based pre-trained model. Secondly, the model uses attention based mechanism and ensemble feature selection to prioritize the relevant features for improving the classification performance. Thirdly, proposed model incorporates the feature fusion of both the images (normal and segmented) to increase the selected feature for better classification. The proposed model achieved high accuracy across multiple datasets, with an accuracy of 99.62%, 99.54%, 99.39%, and 99.57% on the Figshare, Kaggle, Sartaj, combined dataset respectively. The proposed model demonstrates notable improvements in performance on both datasets. It achieves higher accuracy, precision, recall, and F1-score compared to existing models on the both datasets. The proposed FusionNet demonstrates significant improvements in brain tumor classification performance. The utility of this study lies in its contribution to the scientific community as a robust, efficient tool that advances brain tumor classification, supporting medical professionals in achieving superior diagnostic outcomes.