This research presents an innovative framework
that uses blockchain technology to improve tumor segmentation
in medical imaging. The approach tackles issues related to data
security, particularly when dealing with real private dataset,
annotation accuracy, and collaboration. With the growing
reliance of the medical industry on accurate tumor
segmentation from medical images for cancer diagnosis and
treatment, current methods are inadequate in maintaining data
accuracy and promoting collaboration among experts across
different countries. Our suggested approach utilizes blockchain
technology to establish a decentralized, secure platform for the
collaborative obtaining, annotation, and validation of medical
images by data scientists, oncologists, and radiologists. Smart
contracts streamline essential procedures such as verification of
annotations, consensus among experts, and remuneration of
contributors, guaranteeing the dependability and excellence of
the data. Furthermore, the unchangeable record of transactions
in the blockchain ensures a reliable basis for implementing
artificial intelligence and machine learning algorithms. This
improves the accuracy of segmenting data and allows for
predictive modeling. This strategy not only improves the
precision and effectiveness of tumor segmentation but also
promotes a worldwide collaborative environment, which has the
potential to revolutionize cancer diagnostics and treatment
planning. Furthermore, it ensures the privacy and security of
patient data.