The week's pick
Reseach Article
Blockchain-Enabled Quantum-Auto GAN Framework for Secure and Robust Lung Cancer Diagnosis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 144 |
| Year of Publication: 2026 |
| Authors: Ankit Mathur, Karuna Rani, Sunil Kumar Yadav, Ahad Abdullah |
10.5120/ijcacedf930e36fe
|
Ankit Mathur, Karuna Rani, Sunil Kumar Yadav, Ahad Abdullah . Blockchain-Enabled Quantum-Auto GAN Framework for Secure and Robust Lung Cancer Diagnosis. International Journal of Computer Applications. 187, 144 ( Sep 2026), 1-6. DOI=10.5120/ijcacedf930e36fe
Abstract
Medical image diagnosis has evolved to become a vital resource in contemporary healthcare, facilitating early diagnosis and treatment of life-threatening diseases like lung cancer. Secure Medical Image Diagnosis aims at the creation of frameworks that not only provide high diagnostic accuracy but also maintain patient privacy and integrity of data. Current models suffer from several limitations, such as over fitting from small annotated datasets, elevated computational complexity of quantum-based models, inefficiencies in blockchain consensus algorithms, and privacy risk when handling sensitive medical images. To address these limitations, this work introduces an innovative integrated framework that brings together a hybrid Quantum-Auto GAN model and blockchain technology (Q-AGAN-BT). The Q-AGAN-BT carries out latent feature extraction, synthetic image generation, and quantum-boosted classification to enhance diagnostic accuracy and robustness, and blockchain provides safe storage, privacy protection in the form of encrypted off-chain data, access control based on smart contracts, and an innovative Proof of Medical Consensus (PoMC) for light-weight clinically authenticated validation. The novelty of this work is in presenting a very accurate, privacy-enabled, and tamper-resistant lung cancer diagnosis system that overcomes the drawbacks of current approaches and improves reliability and trustfulness in medical image handling. The Q-AGAN-BT model attained the best performance with 95.6% accuracy, 95.0% F1-score, and 0.98 AUC, surpassing classical and hybrid models like CNN + LSTM, Autoencoder + GAN, ResNet50, DenseNet121, and VGG16. The blockchain part provided secure and effective data handling with 0.12 s per-image encryption time, 0.03 s hash validation, 0.08 s smart contract delay, 85% storage savings, and 0.15 s PoMC verification per block, providing high diagnostic performance as well as strong and tamper-proof medical data processing.
References
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