Segmentation and Classification of Medical Images Using Artificial Intelligence: A Review

Abstract

In the realm of medical imaging, segmenting and classifying medical images is essential for helping medical professionals diagnose and treat a variety of medical disorders. This review discusses artificial intelligence (AI) techniques. AI systems have shown impressive accuracy and efficiency in identifying and quantifying elements in medical images, such as MRI, CT, and X-rays, by utilizing deep learning techniques, in particular convolutional neural network (CNNs). We describe CNN design and training for medical image segmentation and classification, emphasizing the usefulness of CNNs in identifying and defining diseased regions and anatomical features. Even with obstacles like data privacy, requiring sizable annotated datasets, and requiring model interpretability, further research and development in AL-driven medical images analysis has promise for improving clinical decision-making and diagnostic accuracy. Future research in this area should concentrate on improving the generalizability and resilience of AI models by using methods like data augmentation, transfer learning, and the creation of more complex network topologies. Furthering the area also requires guaranteeing ethical concerns, enhancing data-sharing mechanisms, and encouraging cooperation between medical practitioners and AI researchers.