This paper proposes a medical image encryption scheme based on Gray code scrambling and block chaotic scrambling Gray+block chaotic scrambling optimized for medical image encryption（GBCS）， which is applied to privacy protection classification. First， the image is sliced by bit-planes.Then， different bit-planes of images are scrambled by the Gray code and then divided into blocks， and chaotic encryption is carried out on these blocks. Finally， the encrypted images are classified by deep learning network. We quantitatively analyze the privacy protection and classification performance of GBCS through cross-validation simulation on public breast cancer and glaucoma datasets， and perform a safety analysis of the method by histogram， information entropy， and anti-attack ability. The experimental results prove the effectiveness of our method. The performance gap of medical images before and after GBCS encryption are within an acceptable range. The proposed scheme can better balance the contradiction between performance and privacy protection requirements， and effectively resist the attack of adversarial samples.