Analysis of Protecting Lung Cancer Images Using Visual Cryptography

Authors

  • V. Sreeprada Department of Computer Science, GITAM Deemed to be University, INDIA Visakhapatnam, Andhra Pradesh, India
  • K. Vedavathi Department of Computer Science, GITAM Deemed to be University, INDIA Visakhapatnam, Andhra Pradesh, India

Keywords:

Lung cancer image, encryption, decryption, security, visual cryptography

Abstract

Linking the intelligent sensors and smart devices in the hospital ecosystem, medical data must be kept securely. To safeguard the privacy of patients, an intelligent working system requires improved security measures. Recently, sure imagine encryption methods based on compression sensing (CS) have been developed for securing images with visual security. In these schemes, both compression and encryption of the images are performed simultaneously, before being fixed into a holder image. The encrypted images are partial by the current systems' presentation limits along with quality and efficiency of the reconstructed images. This paper develops an Improved Visually Secure Image Encryption (IVSIC) scheme which aims to handover real-time lung-cancer images securely, without degrading their quality. The greatest level of security of our proposed system is established by contrasts and imitations. In contrast to some newly developed schemes, it is also more effective and produces cipher and reconstructed images of higher quality.

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References

2nd ed., G. Lima, Verusca S., Francisco Madeiro, and Juliano B. Lima. "Encryption of 3D medical images based on a novel multiparameter cosine number transform." Computers in biology and medicine 121 (2020): 103772.

Tiankai, Sun, Wang Xingyuan, Jiang Daihong, Lin Da, Ding Bin, and Li Dan. "A robust authentication algorithm for medical images based on fractal Brownian model and visual cryptography." Scientific Programming 2020 (2020): 1-11.

Memon, Nisar Ahmed, and Ali Alzahrani. "Prediction-based reversible watermarking of CT scan images for content authentication and copyright protection." Ieee Access 8 (2020): 75448-75462.

Al-Otaibi, May A., Hesham Alhumyani, Saleh Ibrahim, and Alaa M. Abbas. "Efficient Medical Image Encryption Framework against Occlusion Attack." Intelligent Automation & Soft Computing 34, no. 3 (2022).

Li, Jian, Zelin Zhang, Shengyu Li, Ryan Benton, Yulong Huang, Mohan Vamsi Kasukurthi, Dongqi Li et al. "A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology." BMC Medical Informatics and Decision Making 20 (2020): 1-16.

Ding, Yi, Guozheng Wu, Dajiang Chen, Ning Zhang, Linpeng Gong, Mingsheng Cao, and Zhiguang Qin. "DeepEDN: A deep-learning-based image encryption and decryption network for internet of medical things." IEEE Internet of Things Journal 8, no. 3 (2020): 1504-1518.

Khan, Jalaluddin, Jianping Li, Amin Ul Haq, Shadma Parveen, Ghufran Ahmad Khan, Mohammad Shahid, Happy N. Monday, Sana Ullah, and Sun Ruinan. "Medical image encryption into smart healthcare IoT system." In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, pp. 378-382. IEEE, 2019.

Reyad, Omar, and Mohamed Esmail Karar. "Secure CT-image encryption for COVID-19 infections using HBBS-based multiple key-streams." Arabian Journal for Science and Engineering 46, no. 4 (2021): 3581-3593.

Chen, Pi-Yun, Jian-Xing Wu, Chien-Ming Li, Chao-Lin Kuo, Neng-Sheng Pai, and Chia-Hung Lin. "Symmetric Cryptography With Shift 2 n-1, Hash Transformation, Optimization-Based Controller for Medical Image Infosecurity: Case Study in Mammographic Image." IEEE Photonics Journal 12, no. 3 (2020): 1-15.

Lin, Chia-Hung, Jian-Xing Wu, Pi-Yun Chen, Hsiang-Yueh Lai, Chien-Ming Li, Chao-Lin Kuo, and Neng-Sheng Pai. "Intelligent symmetric cryptography with chaotic map and quantum based key generator for medical images Infosecurity." IEEE Access 9 (2021): 118624-118639.

Lakshmanaprabu, S. K., Sachi Nandan Mohanty, K. Shankar, N. Arunkumar, and Gustavo Ramirez. "Optimal deep learning model for classification of lung cancer on CT images." Future Generation Computer Systems 92 (2019): 374-382.

AL-Huseiny, Muayed S., and Ahmed Sattar Sajit. "Transfer learning with GoogLeNet for detection of lung cancer." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1078-1086.

Shanid, Malayil, and A. Anitha. "Adaptive optimisation driven deep belief networks for lung cancer detection and severity level classification." International Journal of Bio-Inspired Computation 18, no. 2 (2021): 114-121.

Berwer, Rajiv Kumar, Sanjeev Indora, and Dinesh Kumar Atal. "Blockchain-Based Medical Image Processing Mechanism." In 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp. 38-43. IEEE, 2022.

Abdul Rahman, Artificial Intelligence in Drug Discovery and Personalized Medicine , Machine Learning Applications Conference Proceedings, Vol 1 2021.

Steffy, A. D. . (2021). Dimensionality Reduction Based Diabetes Detection Using Feature Selection and Machine Learning Architectures. Research Journal of Computer Systems and Engineering, 2(2), 45:50. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/32

Juneja, V., Singh, S., Jain, V., Pandey, K.K., Dhabliya, D., Gupta, A., Pandey, D. Optimization-based data science for an IoT service applicable in smart cities (2023) Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities, pp. 300-321.

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Published

25.12.2023

How to Cite

Sreeprada, V. ., & Vedavathi , K. . (2023). Analysis of Protecting Lung Cancer Images Using Visual Cryptography. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 339–345. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3908

Issue

Vol. 12 No. 1 (2024)

Section

Research Article

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