TRANSFORMER SVS.CNN SIN MEDICAL IMAGING: A COMPARATIVE REVIEW

Deep learning has profoundly changed medical image analysis, with Convolutional Neural Networks (CNNs) serving as the conventional benchmark for tasks such as classification, segmentation, and detection. Lately, Transformers initially created for natural language processing have demonstrated significant achievements in computer vision and are currently being examined for medical imaging thanks to their capacity to grasp global context via self- attention methods. This review offers an extensive comparison between CNNs and Transformers inmedical imaging, emphasizing their structural variations, advantages and drawbacks. CNNsare proficient in local feature extraction and work well with small datasets, but frequently have difficulty in detecting long range dependencies .Conversely, Transformers excel at capturing global relationships, but they necessitate extensive datasets and significant computational power. Hybrid models that merge both architecturesareal so examined, presenting a promising gave nun to exploit their complementary advantages. This review seeks to assist researchers in choosing or creating suitable deep learning architectures for different medical imaging uses, concentrating on enhancing diagnostic precision and clinical significance.