TY - JOUR
T1 - Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics
AU - Chen, Jingxue
AU - Zhao, Pengbiao
AU - Deng, Erqiang
AU - Khan, Fazlullah
AU - Alturki, Ryan
AU - Alshawi, Bandar
AU - Xiong, Hu
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - —Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE.
AB - —Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE.
KW - Fuzzy Deep Learning
KW - Information Steganography
KW - Medical Consumer Electronics
KW - Medical Image Segmentation
KW - Privacy Protection
UR - http://www.scopus.com/inward/record.url?scp=85214673635&partnerID=8YFLogxK
U2 - 10.1109/TCE.2025.3525536
DO - 10.1109/TCE.2025.3525536
M3 - Article
AN - SCOPUS:85214673635
SN - 0098-3063
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
ER -