Pathological Analysis of Brain Tumor in MRI Images Using CAR-UNET Model Architecture

Abstract

The most dangerous type of organ failure in humans is a brain tumor. A brain tumor’s incorrect segmentation and classification are critical for treatment and early diagnosis. Several Deep neural network-based architectures have recently been developed to improve brain tumor classification performance. However, brain tumor classification performance must be improved, which is a difficult area of research. The goal of this study is to analyze different types of brain tumors and how to classify them to increase the survival rate of people with brain tumors. The CAR-U-Net (Concatenation and Residual) image classification method is proposed in this paper to help with brain tumor segmentation and classification research. The baseline U-Net architecture employs concatenation and residual connections. The changes in the network help in discovering varied features by expanding the specific receptive field. We consider two factors for a better diagnosis system: finding missing feature maps and eliminating unfeatured feature maps. The residual connection can solve the over learn or Null feature map problem, while the concatenation connection can solve the missing feature maps problems. This model has been tested on the BraTS2017 Challenge datasets. The network’s concatenation and residual connections, which are used for better deep supervision and tumor differentiation, are accurate. The accuracy, sensitivity, specificity, and dice score were used to compare the performance quantitatively. The proposed system achieved 94.12% Accuracy and 97.16% sensitivity which is higher than the existing systems such as U-net, Residual U-net and, Bayesian SVNN. Also, the proposed system got the dice score coefficient of 90.32. which is higher compared to conventional U-net and Residual U-net. The proposed CAR-UNET model outperformed current best-practice techniques. The high-performance capacity of this model can help bioinformatics, medicine, and early diagnosis researchers.