Soft sensors for screening and detection of pancreatic tumor using nanoimaging and deep learning neural networks

Screening of nanosized pancreatic tumors, both benign and malignant, is a very important issue in the medical field because it is directly influences the digestive system, which has an effect on human health. Nanobiosensor is the application of nanotechnology in the medical field. This is a multidisciplinary field that currently involves nanotechnology and biomedical applications. The malfunctioning of the pancreas is a health concern because it helps maintain the blood glucose level at a nominal value. Day-to-day food habits have driven the need to develop rapid, responsive, and reliable methods to detect pancreatic tumors. The rapid development of nanosensors that have an advantage to detect variations in the texture of the pancreas in nanometers has paved way to diagnose a malfunctioning pancreas at the onset stage linking nanosensors with modern Information and Communication Technologies (ICTs) enabling novel and online ways of detection accompanied with high accuracy. Various types of nanosensors are being developed to meet different requirements in the field of medicine for detection of various abnormalities related to the organs of the human body. Detection of nanosized pancreatic tumors is the focus of this work. The existence of nanosized pancreatic tumors in the patient leads to early diagnosis. If the tumor is identified in the chronic stage, the chances of survival of the patient are very less. Detection of nanosized tumors will enhance the analysis, diagnosis, and prognosis at the onset stage leading to suitable and timely medication. Currently, this work depends on feature analysis of Magnetic Resonance Imaging (MRI) images obtained from the database to identify the nanosized pancreatic tumors at the onset stage. A distinct diagnosis method is proposed for identification of pancreatic tumors using image texture characters, which were statistically evaluated using MATLAB. Diagnosis was done using Deep Wavelet Neural Networks (DWNN). Using DWNN method, combined with intelligent and pattern recognition algorithms, nearly 99% of sensitivity in the detection of nanosized pancreatic tumors is achieved.