Secure Medical Imaging Data using Cryptography with Classification


  • Saqqiya Waris Quaid e Azam College, Wah campus, Wah Cantt
  • Syeda Aleena Naqvi Ravaile communication and media, Islamabad, Islamabad Capital Territory 46000, Pakistan


Image Classification, pre-processing, Features extraction, MRI images, Cryptography, Brain-Tumor, AlexNet, CNN


Medical imaging data is increasing day by day which requires improved applications that perform accurate diagnoses. Secure medical imaging data plays a critical role in current times. Still, today it is a complex task to maintain data privacy, so this study's main objective is to solve this problem. In this research, firstly, secure the medical imaging data using the cryptography Advance Encryption Standard (AES) algorithm. In this process, input images are encrypted and decrypted using public key cryptography and supplied as input to the pre-trained convolutional neural Network such as Alex-net. The model comprises 25 layers such as convolutional, batch-normalization, ReLU and max-pooling, etc. The classification between the tumor and healthy images has been performed using the SoftMax layer. The performance of the proposed model has been tested on the publicly available BRATS-2020 Challenging dataset. The proposed model achieved up to 99.71% accuracy and 97% F1- scores, which are far better as compared to the latest published research work in this domain.

Keywords: Image Classification; Preprocessing; Features extraction; Cryptography; MRI images

Author Biography

Syeda Aleena Naqvi , Ravaile communication and media, Islamabad, Islamabad Capital Territory 46000, Pakistan




H. Mohsen, E.-S. A. El-Dahshan, E.-S. M. El-Horbaty, and A.-B. M. Salem, "Classification using deep learning neural networks for brain tumors," Future Computing and Informatics Journal, vol. 3, pp. 68-71, 2018.

A. Rehman, S. Naz, M. I. Razzak, F. Akram, and M. Imran, "A deep learning-based framework for automatic brain tumors classification using transfer learning," Circuits, Systems, and Signal Processing, vol. 39, pp. 757-775, 2020.

M. M. Badža and M. Č. Barjaktarović, "Classification of brain tumors from MRI images using a convolutional neural network," Applied Sciences, vol. 10, pp. 1999-2020, 2020.

N. Alassaf, B. Alkazemi, and A. Gutub, "Applicable light-weight cryptography to secure medical data in IoT systems," Arabia, vol. 2, pp. 50-58, 2003.

M. Elhoseny, G. Ramírez-González, O. M. Abu-Elnasr, S. A. Shawkat, N. Arunkumar, and A. Farouk, "Secure medical data transmission model for IoT-based healthcare systems," Ieee Access, vol. 6, pp. 20596-20608, 2018.

A. A. Abd-Alrazaq, M. Alajlani, D. Alhuwail, A. Erbad, A. Giannicchi, Z. Shah, et al., "Blockchain technologies to mitigate COVID-19 challenges: A scoping review," Computer methods and programs in biomedicine update, vol. 1, pp. 100001-100014, 2021.

D. C. Nguyen, M. Ding, P. N. Pathirana, and A. Seneviratne, "Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: A survey," Ieee Access, vol. 9, pp. 95730-95753, 2021.

R. Kumar, A. A. Khan, J. Kumar, N. A. Golilarz, S. Zhang, Y. Ting, et al., "Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging," IEEE Sensors Journal, vol. 21, pp. 16301-16314, 2021.

J. Amin, M. Sharif, A. Haldorai, M. Yasmin, and R. S. Nayak, "Brain tumor detection and classification using machine learning: a comprehensive survey," Complex & intelligent systems, vol. 8, pp. 3161-3183, 2022.

S. Solanki, U. P. Singh, S. S. Chouhan, and S. Jain, "Brain Tumor Detection and Classification using Intelligence Techniques: An Overview," IEEE Access,pp. 12870 - 12886, 2023.

V. P. Reddy, R. M. Prasad, P. Udayaraju, B. H. Naik, and C. Raja, "Efficient medical image security and transmission using modified LZW compression and ECDH-AES for telemedicine applications," Soft Computing, pp. 1-18, 2023.

Y. V. Lakshmi, K. Naveena, M. Ramya, N. Pravallika, T. Sindhu, and V. Namitha, "Medical Image Encryption using Enhanced Rivest Shamir Adleman Algorithm," in 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 1-5, 2023.

F. Mohammad, S. Al Ahmadi, and J. Al Muhtadi, "Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans," Diagnostics, vol. 13, pp. 1229- 1236, 2023.

T. Balamurugan and E. Gnanamanoharan, "Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier," Neural Computing and Applications, vol. 35, pp. 4739-4753, 2023.

I. Shahzadi, T. B. Tang, F. Meriadeau, and A. Quyyum, "CNN-LSTM: Cascaded framework for brain tumour classification," in 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 633-637, 2018.

J. Amin, M. A. Anjum, N. Gul, and M. Sharif, "Detection of brain space-occupying lesions using quantum machine learning," Neural Computing and Applications, pp. 1-17, 2023.

J. Amin, M. Sharif, M. Raza, and M. Yasmin, "Detection of brain tumor based on features fusion and machine learning," Journal of Ambient Intelligence and Humanized Computing, pp. 1-17, 2018.

J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, "A distinctive approach in brain tumor detection and classification using MRI," Pattern Recognition Letters, vol. 139, pp. 118-127, 2020.

J. Amin, M. Sharif, M. Raza, T. Saba, R. Sial, and S. A. Shad, "Brain tumor detection: a long short-term memory (LSTM)-based learning model," Neural Computing and Applications, vol. 32, pp. 15965-15973, 2020.

M. Sharif, J. Amin, M. Raza, M. A. Anjum, H. Afzal, and S. A. Shad, "Brain tumor detection based on extreme learning," Neural Computing and Applications, vol. 32, pp. 15975-15987, 2020.

J. Amin, M. Sharif, M. Raza, T. Saba, and A. Rehman, "Brain tumor classification: feature fusion," in 2019 international conference on computer and information sciences (ICCIS), pp. 1-6, 2019.

S. Z. Kurdi, M. H. Ali, M. M. Jaber, T. Saba, A. Rehman, and R. Damaševičius, "Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks," Journal of Personalized Medicine, vol. 13, pp. 181-194, 2023.

A. Omotosho, J. Emuoyibofarhe, and C. Meinel, "Ensuring patients' privacy in a cryptographic-based-electronic health records using bio-cryptography," International Journal of Electronic Healthcare, vol. 9, pp. 227-254, 2017.

M. A. Khan, A. Khan, M. Alhaisoni, A. Alqahtani, S. Alsubai, M. Alharbi, et al., "Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm," International Journal of Imaging Systems and Technology, vol. 33, pp. 572-587, 2023.

P. K. Ramtekkar, A. Pandey, and M. K. Pawar, "Innovative brain tumor detection using optimized deep learning techniques," International Journal of System Assurance Engineering and Management, vol. 14, pp. 459-473, 2023.

N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, "Brain tumor classification using convolutional neural network," in World Congress on Medical Physics and Biomedical Engineering 2018: June 3-8, 2018, Prague, Czech Republic, vol 1, pp. 183-189, 2019.

J. Seetha and S. S. Raja, "Brain tumor classification using convolutional neural networks," Biomedical & Pharmacology Journal, vol. 11, pp. 1457-1264, 2018.

N. V. Chavan, B. Jadhav, and P. Patil, "Detection and classification of brain tumors," International Journal of Computer Applications, vol. 112, pp. 48-53, 2015.

E. S. Hureib and A. A. Gutub, "Enhancing medical data security via combining elliptic curve cryptography and image steganography," Int. J. Comput. Sci. Netw. Secur.(IJCSNS), vol. 20, pp. 1-8, 2020.

M. Puppala, T. He, X. Yu, S. Chen, R. Ogunti, and S. T. Wong, "Data security and privacy management in healthcare applications and clinical data warehouse environment," in 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 5-8, 2016.

A. Lewko, T. Okamoto, A. Sahai, K. Takashima, and B. Waters, "Fully secure functional encryption: Attribute-based encryption and (hierarchical) inner product encryption," in Advances in Cryptology–EUROCRYPT 2010: 29th Annual International Conference on the Theory and Applications of Cryptographic Techniques, French Riviera, May 30–June 3, 2010. Proceedings 29, 2010, pp. 62-91.

A. Kosba, A. Miller, E. Shi, Z. Wen, and C. Papamanthou, "Hawk: The blockchain model of cryptography and privacy-preserving smart contracts," in 2016 IEEE symposium on security and privacy (SP), 2016, pp. 839-858.

D. Coppersmith, "The Data Encryption Standard (DES) and its strength against attacks," IBM journal of research and development, vol. 38, pp. 243-250, 1994.

B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., "The multimodal brain tumor image segmentation benchmark (BRATS)," IEEE transactions on medical imaging, vol. 34, pp. 1993-2024, 2014.

N. Ayache, N. Cordier, and H. Delingette, "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)," 2014.

Saeed, T., Kiong Loo, C. and Safiruz Kassim, M.S., "Ensembles of Deep Learning Framework for Stomach Abnormalities Classification," Computers, Materials & Continua, 70(3). pp. 4357- 4372, 2022




How to Cite

Saqqiya Waris, & Naqvi , S. A. . (2023). Secure Medical Imaging Data using Cryptography with Classification. University of Wah Journal of Computer Science, 5, 1–13. Retrieved from