Fraudulence Detection of Medical Images using Pixel Level Algorithm

Abstract

Healthcare is one of the important, sensitive and superior department for a country. While the healthcare sector is developing, security and privacy becomes our major concern. Trust between the doctor and patient is the major foundation in medical field. This type of fraudulence may put that trust at risk by providing wrong treatment on the basis of the forged images. Technologies like these makes the job easier, establish strong trust and secure client’s dignity. Our project gives a system of fraudulence detection of medical images for the healthcare department to verify that images related to health are not tampered, changed or altered. In our model we used the Hybrid Median-filter-based noise reduction technique. Our model constitutes SVM+ELM classifiers and their combined result is subjected to Bayesian sum rule and whether fraudulence is involved or not is decided.