MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641063603 A) filed by Dr. P. Hema Kumar Associate Professor Department Of Ece; Kotteeswaran Rangasamy Associate Professor Department; Ms. S. Anslam Sibi Assistant Professor Department Of Computer Science And Engineering; Manju Bagga, Assistant Professor, Department Of Mca; Dr. Divvela Srinivasa Rao, Associate Professor,department Of Ai&ds; and E. Muthuramalingam, Apeie on May 20, 2026, for A Deep Learning Driven Color Space Encryption Method For Medical Images In Cloud Healthcare Environments.
Inventors include Dr. P. Hema Kumar Associate Professor Department Of; Kotteeswaran Rangasamy Associate Professor Department; Ms. S. Anslam Sibi Assistant Professor Department Of; Manju Bagga, Assistant Professor, Department Of Mca; Dr. Divvela Srinivasa Rao, Associate Professor,department Of Ai&ds; and E. Muthuramalingam, Apeie.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The information and intelligence of wisdom medicine have gradually been attained by utilizing big data, cloud computing, artificial intelligence, and other technologies. However, information security issues have become more crucial due to the transmission and storage of massive amounts of medical images on the cloud. The risk of revealing, stealing, or otherwise tampering with patients' private information has emerged as a significant roadblock to the advancement of medical science. It is imperative to address the critical issue of protecting patient data in the cloud environment. Because they require less processing power and are JPEG- compatible, color space-based scrambling algorithms (CSSA) are becoming more popular for encrypting multimedia data. Traditional ways need a colorful image as input to decrease block size and increase security. Due to the absence of color images, CSSA approaches are insufficient for secure transmission and storage in fields like medical image processing. This study aims to merge CSSA image encryption applications with Multilayer Perceptron (MLP)-based medical image encryption techniques. We developed a noise-based data augmentation method for medical image analysis to alleviate data limitations. Security analysis and temporal complexity are used to examine the effectiveness of our proposed (MLP-CSSA) deep learning model for encrypting medical images. The results demonstrate a high level of security achieved by encrypting both black-and-white and color medical images. A comparison is provided between many encryption techniques. The proposed encryption method MLP-CSSA beats the most recent existing techniques for encrypting medical photos. Keywords:Deep learning, Medical images, Colour space-based scrambling algorithms (CSSA), JPEG standard, Multilayer Perceptron's (MLP), encryption techniques
Disclaimer: Curated by HT Syndication.
