Deep Learning Approaches For Efficient Tumor Segmentation In Medical Imaging

Abstract

This paper looks into the most advanced deep learning methods developed for better and even automated tumor segmentation along with their merits, obstacles and improvements. Accurate and timely tumor segmentation from medical images is crucial for the diagnosis, treatmentand follow-up of diseases. Manual segmentation and other traditional methods of segmentation involve a great deal of time and effort which often leads to inconsistencies. Computer science has brought deep learning and especially convolutional neural networks  and transformer-based models which are game changers for automated and accurate tumor segmentation. The writer investigates a number of different AI architectures for deep learning tumor segmentation, including U-Net, fully convolutional networks and attention models. The publicly available datasets offer medical imaging information for brain and liver tumors, respectively. Evaluation is accomplished using metrics defined as Dice Similarity Coefficient  IoU and sensitivity. CNN-based models such as U-Net are still dominant, but transformer-based architectures are emerging due to their ability to handle long-range dependencies in complex tumor structures. The shortage of annotated data, high computational demand and lack of generalizability across different imaging sensors are prevalent. Further research is needed into domain adaptation, semi-supervised learning, and XAI to improve the usability of deep learning in medical imaging.