Advancing Pancreatic Cancer Diagnosis with Artificial Neural Networks: Current Research and Future Prospects
Kamal Upreti, Mohammad Nasir, Mohammad Shabbir Alam, Fazal Imam Shahi, Prashant Vats, Ashok Kumar Saini
Abstract
With the use of medical image analysis, Artificial Neural Networks (ANNs) have shown effective results in the early detection and treatment of pancreatic cancer. A summary of current studies using ANNs for AI-driven pancreatitis cancer detection is given in this research study. Numerous studies have shown that ANNs could always identify and diagnose pancreatic cancer with high accuracy when utilizing CT scans, MRIs, and other types of medical scanning. The early detection of pancreatic cancer using ANNs is very useful for enhancing outcomes for patients. The design and implementation of ANNs for the detection of pancreatic cancer does not come without difficulties, though. These include the demand for extensive and varied datasets, the necessity for ongoing model training, and the necessity for point procedures to guarantee the precision and dependability of ANN-based diagnostic tools. In summary, using ANNs for AI-driven pancreatic detection of cancer has enormous potential to enhance outcomes for patients. These technologies need to be improved and validated, and the ethical and legal issues surrounding their usage in therapeutic contexts need to be addressed.