Accurate and Efficient Brain Tumor Segmentation using transfer learning and residual networks

Accurate and specific segmentation of brain tumour is essential in medical imaging for accurate diagnosis and planning of treatments. In this research paper, we suggest the use of a Densenet121 model for tumor recognition and a ResUnet model for segmentation. We fine-tune the DenseNet121 model using transfer learning techniques on a new dataset, with pre-trained weights obtained from a large-scale dataset (ImageNet). By leveraging the strengths of transfer learning with Densenet121’s and ResUnet’s robust feature extraction capabilities, our model effectively detects and segments tumors from Brain MRI Images. This approach can speed up the exercise procedure and improve the model’s accuracy, especially for small datasets. We validated our approach on “The Cancer Genome Atlas (TCGA)” dataset from The Cancer Imaging Archive (TCIA) and compared it with several classification and segmentation deep learning models. The experimental results on the testing data demonstrate that our approach is more efficient and accurate than other deep learning networks. This approach can substantially accelerate the training process and enhance the accuracy of the model, particularly in scenarios where the new dataset is small. We have validated our proposed approach on “The Cancer Genome Atlas (TCGA)” dataset from The Cancer Imaging Archive (TCIA). We have compared our approach with various classification and segmentation deep learning models, and the experimental results on the testing data demonstrate that our method performs better in terms of precision and effectiveness than other deep learning networks.