International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | MEDICRYPT: Concealing and Securing Patient Data within Medical Images |
|---|---|
| ABSTRACT | Medical images shared across digital healthcare systems often carry visible patient identifiers that conventional encryption and steganography fail to protect together. This paper presents MEDICRYPT, a privacy-preserving framework that unites Optical Character Recognition (OCR), Gaussian Blur-based redaction, and Least Significant Bit (LSB) steganography. OCR locates patient text on chest X-ray images, Gaussian Blur conceals the detected regions, and the extracted particulars are embedded as a binary payload through LSB. Evaluation on the COVID-19 Chest X-ray Radiography dataset yields high PSNR and SSIM with low MSE, confirming that visible and hidden patient data are protected while diagnostic quality is preserved. |
| AUTHOR | KOULAMPETA YAKSHITHA, DR. M. NAGARATNA PG Student, Dept. of Computer Science and Engineering, JNTUH University College of Engineering, Science and Technology, Hyderabad, Telangana, India Professor, Dept. of Computer Science and Engineering, JNTUH University College of Engineering, Science and Technology, Hyderabad, Telangana, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406089 |
| pdf/89_MEDICRYPT Concealing and Securing Patient Data within Medical Images.pdf | |
| KEYWORDS | |
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