An Efficient Counterfeit Medicine Classification Forecasting System: A Structure based Deep Learning Technique

Abstract

Identification of fake medicine images with chemical structure by using different compounds or molecular compositions that are different from the legitimate products. Thus, by using better computational approaches to look into structural features can help in the detection of counterfeit medicine. The proposed work in this paper enhances the ability to distinguish between the original and fake product that is crucial for product legitimacy identification and consumer health especially with industries dealing with pharmaceutical products and construction materials while public safety demands counterfeit medicine identification. The current techniques prove to be inefficient and the precise outcomes can be achieved by using complex calculations derived from the chemical structures. The aim of this study is to develop an efficient system based on Graph Neural Networks (GNN)’s to classify and predict counterfeit medicines for addressing the global counterfeit medicines issue. This paper proposes the classification system of counterfeit medicines based on the chemical structure and its forecast is assisted by the Deep Learning method known as GNN. The given methodology incorporates pre-processing steps which enhance structural characteristics of chemical compounds. The edge detection algorithms such as the Canny edge detector emphasize the prominent structural features. The morphological operation, dilation, and erosion are used for improvement of these features. The proposed chemical structure-based counterfeit medicine image detection using Canny Edge Detector with Graph Neural Networks (CED-GNN) is found to be better than the existing techniques with a maximum accuracy of 81.91%.