Privacy preserving medical image classification and steganography using deep learning architecture: A pipeline

This study addresses significant data privacy issues in telemedicine by proposing a secure method for transmitting sensitive medical information over unsecured networks. The novel pipeline combines state-of-the-art image classification with image steganography to integrate patient diagnosis and personal information into medical images, ensuring data privacy and protection from unauthorized access. The approach uses fine-tuned convolutional models for classification and an encoder-decoder architecture for steganography, safeguarding the transmission of medical data while maintaining image quality.