Earlier Detection of Pancreatic Cancer Using Neural Network Based Optimization Technique

Medical healthcare systems are being extensively studied, giving computer technology lots of space to innovate. The most important medical research is predicting cancer, which may take numerous forms and affect many body parts. One of the most common fatal diseases is pancreatic cancer, which cannot be cured once identified and is sometimes unexpected since it is placed in the abdomen beyond the stomach. CT and MRI often give CAD, quantitative evaluations, and automated pancreatic cancer segmentation. These cancer classification methods might identify, predict, and assist personalized medicine to cure cancer without malignant invasions. Flying Squirrel optimization segments, extracts, and classifies features. CNN coupled with Frog Leap optimization. The proposed approach uses frog leap optimization to identify picture normalcy and abnormality. The suggested method minimizes errors for correct classification. Segmenting the aberrant picture with an adaptive flying squirrel algorithm determines cancer size and severity. The more efficient CNN-FLFS method predicts pancreatic cancer with 99% accuracy.