MRI Brain Tumor Classification and Extraction using Deep Learning-Based Decision Level Image Fusion Technique

Abstract

Objectives: The proposed work emphasizes the tumor region extracted from the multimodal MRI brain scan by deep learning-based decision-level image fusion technique. Methods: Convolutional Neural Network (CNN) architectures such as AlexNet, ResNet50, and VGG16 perform brain tumor classification with multimodal MRI images Flair, T2, and T1c respectively. Flair images are fed to the AlexNet architecture, T2 images are fed to the ResNet50 architecture, and T1c images are fed to the VGG16 architecture to classify brain tumor images. The classification results from these architectures are fused together to perform the decision on the given inputs. If the inputs come under the decision of the tumor affected then the tumor portion will be extracted using the fusion of three images as a post-processing operation. Findings: The experiments are done using BraTS datasets an open-access brain tumor image analysis research repository. The three CNN architectures' performance is measured by accuracy and gives 0.87 for AlexNet, 0.91 for ResNet50, and 0.99 for VGG16. The extracted tumor region from the fused output image is compared with the ground truth image by metrics such as SSIM with 0.93, DC 0.96, and PSNR with 66.57. Better results are received for the proposed work in the evaluation analysis than the existing works. Novelty: Decision level image fusion limitedly experimented with Deep Learning techniques in state-of-art methods. In this proposed method, the decisions made based on the classification result of three CNN architectures. Keywords: MRI, Brain tumor, Deep Learning, Convolutional Neural Network Architectures, Image Fusion, AlexNet, ResNet50, VGG16