An optimized deep learning model for fast and accurate brain tumor segmentation.

The proper segmentation of brain tumors is essential in medical image analysis field. Convolutional neural networks (CNNs), which make up the majority of deep learning approaches, have demonstrated encouraging results in automating this process. This research paper presents a novel two-step approach for brain segmenting and detecting tumors. In the first step, we employ a DenseNet121 model with transfer learning, fine-tuning the pre-trained weights obtained from the Image Net dataset, to recognize tumors in the brain. The second step involves utilizing a ResUnet model, which combines the strengths of ResNet50 and Unet models, for accurate segmentation. To validate our approach, we conduct experiments on "The Cancer Imaging account (TCIA)'s dataset "The Cancer Genome Atlas (TCGA)" and compare the results with other models of deep learning such as K-means clustering and VGG16. Our experimental findings demonstrate that our proposed approach achieves superior accuracy and efficiency compared to the alternative models. [ABSTRACT FROM AUTHOR]