Advancing Generative AI for Visualizing Drug Addiction: Comparative Analysis and Dataset Innovation

This research was conducted with a view to develop personalized awareness tools which can play an important role against drug abuse by visualizing the potential impacts of addiction. A novel dataset was prepared for this purpose consisting of before-and-after images of individuals affected by drug addiction. Two popular GAN architectures - Pix2Pix and CycleGAN were developed in this research, utilizing transfer learning to enhance training efficiency. A comparative analysis revealed that while Pix2Pix is adept at incorporating distinct addiction-related features into the generated images, it often fails to maintain the facial structure of the individuals. On the contrary, CycleGAN maintains structural integrity but it is less precise in transferring addiction-specific characteristics. These insights reveal both the strengths and weaknesses of using these technologies for personalized medical and awareness applications.