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International Journal of Advances in Engineering & Scientific Research

SMART LEUKEMIA DETECTION SYSTEM USING CONVOLUTION NEURAL NETWORK ALGORITHM

Dr. S. Dhanabal, Ramakrishnan P, Sanjay S, Vinothkanna J R

Dr. S. Dhanabal, Ramakrishnan P, Sanjay S, Vinothkanna J R, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Tamilnadu, India

DOI : https://doi.org/10.5281/zenodo.14928724 Page No : 112-137

Published Online : 2025-02-26

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As cloud computing is becoming more dependent for remote sensing image storage and retrieval, security, integrity, and verifiability of data become a serious challenge. Conventional image retrieval systems are vulnerable to unauthorized access, data tampering, and lack of transparency. To overcome these challenges, this paper introduces a Blockchain-Based Secure and Verifiable Remote Sensing Image Retrieval System in a cloud computing scenario. The methodology to be followed combines blockchain technology with deep learning image retrieval methods to provide better security, trust, and efficiency. The UniProt/Swiss-Prot protein web server and datasets like anti-inflammatory and anticancer datasets were preprocessed by applying CD-HIT clustering to remove redundancy and preserve data integrity. A hybrid deep learning model that combines CNN, RNN, GRU, and MLP models was trained on optimized datasets for high accuracy and strong image classification. To guarantee safe data storage and recovery, blockchain is used to create a decentralized journal to record each transaction of the images, enabling immutability and verifiability. Authentication and access are made automatic with the use of smart contracts that bar unauthorized manipulation and ensure queries are processed openly. The performance of the presented approach is evident through the achieved high specificity, sensitivity, and precision, which shows CNN models having better performance in image classification. By integrating blockchain and deep learning, the system is able to deliver a tamper-proof, scalable, and efficient remote sensing image retrieval mechanism for cloud-based systems, thus a feasible method for secure management of geospatial data and real-time usage.

Keywords: Blockchain, Cloud Computing, Remote Sensing, Image Retrieval, Deep Learning, Smart Contracts, Data Security, Verifiability, CD-HIT, Decentralized Storage

References

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Tran, T., Park, J. H., Kwon, O. H., Moon, K. S., Lee, S. H., & Kwon, K. R. (2018). Classification of leukemia disease in peripheral blood cell images using convolutional neural network. Journal of Korea Multimedia Society, 21(10), 1150-1161.
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Praveena, S., & Singh, S. P. (2020). Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia. Biomedical Engineering/Biomedizinische Technik, 65(6), 759-773.

Dhanabal S, Ramakrishnan P, Sanjay S, & Vinothkanna J R. (2025), Smart Leukemia Detection System Using Convolution Neural Network Algorithm”, International Journal of Advances in Engineering & Scientific Research, Volume 12, Issue 1, 2025, pp 112-137 DOI : https://doi.org/10.5281/zenodo.14928724

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References

Barma MD, Muthupandiyan I, Samuel SR, Amaechi BT. Inhibition of Streptococcus mutans, antioxidant property and cytotoxicity of novel nano-zinc oxide varnish. Arch Oral Biol. 2021 Jun;126:105132. doi: 10.1016/j.archoralbio.2021.105132. Epub 2021 Apr 23.
Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484.
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520.
Avacharmal, R., Pamulaparthyvenkata, S., Ranjan, P., Mulukuntla, S., Balakrishnan, A., Preethi, P., & Gomathi, R. D. (2024, June). Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
Siddique R, Nivedhitha MS, Jacob B. Quantitative analysis for detection of toxic elements in various irrigants, their combination (precipitate), and para-chloroaniline: An inductively coupled plasma mass spectrometry study. J Conserv Dent. 2019 Jul-Aug;22(4):344-350. doi: 10.4103/JCD.JCD_95_19.
Tran, T., Park, J. H., Kwon, O. H., Moon, K. S., Lee, S. H., & Kwon, K. R. (2018). Classification of leukemia disease in peripheral blood cell images using convolutional neural network. Journal of Korea Multimedia Society, 21(10), 1150-1161.
Bibi, N., Sikandar, M., Ud Din, I., Almogren, A., & Ali, S. (2020). IoMT‐based automated detection and classification of leukemia using deep learning. Journal of healthcare engineering, 2020(1), 6648574.
Anilkumar, K. K., Manoj, V. J., & Sagi, T. M. (2021). Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison. Medical Engineering & Physics, 98, 8-19.
Anwar, S., & Alam, A. (2020). A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction. Medical & biological engineering & computing, 58(12), 3113-3121.
Praveena, S., & Singh, S. P. (2020). Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia. Biomedical Engineering/Biomedizinische Technik, 65(6), 759-773.

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