Telemedicine is evolving rapidly and hence the need for quicker transmission of diagnostic clinical images and image processing arises. AI in healthcare has become highly significant with more accurate detection of illnesses using medical imaging datasets. As a result, it helps AI models to understand and track various types of illnesses using computer vision technology. This technology is used mostly with the help of machine learning. Hence to make the clinical imaging datasets functioning for machine learning, distinct types of annotation tools are implied. Semantic image segmentation is one of them to name a few.
Semantic image segmentation annotation helps in annotating the objects for pictorial perception-based models of AI for accurate detections. The medical sector being the crucial one, is directly associated with the health and wellness of the human population. Therefore, dependency on the machine-based illness diagnosis and prediction of the disease gets more vigilant based on accuracy to ensure machines help clinicians take the right decision for the diagnosis.
And hence, the infected body parts or organs in medical images must be annotated or labelled in such a way that deep learning algorithms help in detecting any infection or symptom with accuracy while establishing the AI model.