Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Jyotismita Chaki, Ph.D., is an Assistant Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. She has done her PhD (Engg) from Jadavpur University, Kolkata, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Artificial Intelligence and Machine learning. She has authored more than forty international conferences and journal papers. She is the author and editor of more than five books. Currently she is the academic editor of PLOS ONE journal (IF: 3.24) and Associate editor of IET Image Processing Journal (IF: 2.373), Array journal (Elsevier) and Machine Learning with Applications journal (Elsevier).

1. Introduction to brain tumor segmentation using Deep Learning 2. Data preprocessing methods needed in brain tumor segmentation 3. Transformation of low-resolution brain tumor images into super-resolution images using Deep Learning based methods 4. Single path Convolutional Neural Network based brain tumor segmentation 5. Multi path Convolutional Neural Network based brain tumor segmentation 6. Fully Convolutional Networks (FCNs) based brain tumor segmentation 7. Cascade convolutional neural network-based brain tumor segmentation 8. Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) for brain tumor segmentation 9. Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN) for brain tumor segmentation 10. Generative Adversarial Networks (GAN) based brain tumor segmentation 11. Auto encoder-based brain tumor segmentation 12. Ensemble deep learning model-based brain tumor segmentation 13. Research Issues and Future of Deep Learning based brain tumor segmentation