Evaluating FCN and SegNet for Brain Tumor Segmentation

Accurate segmentation and early identification of brain tumors play a pivotal role in enhancing treatment outcomes and increasing patient survival rates. This study aims to evaluate the performance of two deep learning models, SegNet and Fully Convolutional Networks (FCN), in performing segmentation tasks on MRI images of brain tumors. The comparison highlights their relative performance in addressing this critical medical imaging challenge. The dataset used in this study comprises 1,502 training images and 231 testing images. Dice Loss is employed as the loss function, with the Adam optimizer facilitating the training process. Experimental results demonstrate that the FCN model retains high-resolution features, significantly enhancing segmentation accuracy and generalization capabilities. Although SegNet converges faster during training, its performance on the validation set is slightly inferior to that of the FCN model. Overall, the FCN model exhibits superior performance in brain tumor segmentation, showcasing better detail capture and reduced noise interference. Future research could focus on incorporating larger-scale datasets, state-of-the-art segmentation models, and high-resolution images to further improve segmentation accuracy and practical applications.