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.