Revolutionizing Medical Imaging with Artificial inelegant Real-Time Segmentation for Enhanced Diagnostics

Abstract

Machine Intelligent or AI has become a proved tool with high accuracy and efficiency in medical imaging diagnoses. Thus, this paper aims at developing and analyzing the feasibility of using AI-based real-time image segmentation based on models such as Vision Transformers (ViT) and Convolutional Neural Networks (CNN). Previous attempts on segmentation problems have focused on CNNs, but the self-attention approach in ViT poses a distinct possibility since this archetypal model captures global contexts in images and may be particularly beneficial when dealing with challenging medical data. To assess the effectiveness of these approaches, publicly available datasets including ISIC for skin lesion segmentation and BraTS for brain tumor analysis are used. These datasets are highly challenging because of their shapes, as well as the different image resolutions of objects and their overlapping areas, so they are perfect for evaluating segmentation models. The presented models are trained with TensorFlow and PyTorch, and the accuracy is evaluated in terms of intersection over union (IoU) and Dice coefficient. However, the time required to process one image to analyze the results is taken with a view of establishing real-time applicability. Experiments show that with ViT, the segmentation accuracy is higher than that of CNN and the Dice Score is higher by 0.15 while the computation time is lower by 30%. Bath and space-party enhancements to TOF and MI allow quicker, more accurate diagnoses to be returned to the radiologist and reduce the likelihood of errors. In addition, the paper demonstrates that ViT-based models are resilient to variability in medical imaging tasks while maintaining high accuracy and effectiveness. This study proves the capabilities of AI in healthcare advancement when ViT becomes a part of clinical practice. We will leave future work for the exploration of the mix of models, such as CNN-ViT in order to fine-tune the results for specific diagnostic .