Optimal Hybrid Image Encryption with Machine Learning Model for Blockchain-Assisted Secure Skin Lesion Diagnosis
Optimal Hybrid Image Encryption with Machine Learning Model for Blockchain-Assisted Secure Skin Lesion Diagnosis |
||
 |
 |
|
| © 2023 by IJETT Journal | ||
| Volume-71 Issue-6 |
||
| Year of Publication : 2023 | ||
| Author : K. Rajeshkumar, C. Ananth, N. Mohananthini |
||
| DOI : 10.14445/22315381/IJETT-V71I6P211 | ||
How to Cite?
K. Rajeshkumar, C. Ananth, N. Mohananthini, "Optimal Hybrid Image Encryption with Machine Learning Model for Blockchain-Assisted Secure Skin Lesion Diagnosis," International Journal of Engineering Trends and Technology, vol. 71, no. 6, pp. 96-106, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I6P211
Abstract
Skin cancer is the most prevalent ailment, which is detected initially by visual observation and further using dermoscopy analysis and other tests. The current advancement of Artificial Intelligence (AI) approaches is widely enforced for precisely identifying illnesses in real-time scenarios. Although the advantages, energy constraining, inadequately trained data, and security are the key challenges that need to be solved in the IoT-assisted healthcare field. To achieve this, blockchain (BC) technology has been newly discovered, which is the decentralized structure that has been extensively used. This study presents an Optimal Hybrid Image Encryption with Machine Learning Model for Blockchain Assisted Secure Skin Lesion Diagnosis (OHIEML-BSSLD) system. The major intention of the OHIEML-BSSLD approach is to enable a secure skin lesion detection process via image encryption and BC technology. The presented OHIEML-BSSLD technique initially enables the BC technology to store medical images securely. The OHIEML-BSSLD technique employs an advanced hybrid encryption standard with data encryption standard (HAES-DES) technique with firefly (FF) algorithm-based optimal key generation to improve the security level. For skin lesion detection, the OHIEML-BSSLD technique employs image preprocessing, fully convolutional network (FCN) based semantic segmentation, radiomics features, and optimal Kernel Extreme Learning Machine (KELM) classification. For improving the KELM model performance, the particle swarm optimization (PSO) technique was utilized for parameter tuning purposes. The performance outcome of the OHIEML-BSSLD technique was examined utilizing benchmark skin datasets, and the outputs highlighted the capable performance of the OHIEML-BSSLD technique over other techniques.
Keywords
Medical imaging, Skin lesion diagnosis, Security, Blockchain, Image encryption, Key generation.
References
[1] Selvia et al., “Skin Lesion Detection Using Feature Extraction Approach,” Annals of the Romanian Society for Cell Biology, vol. 25, no. 4, pp. 3939-3951, 2021.
[Google Scholar] [Publisher Link]
[2] Jwan Najeeb Saeed, and Subhi R. M. Zeebaree, “Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures,” Journal of Applied Science and Technology Trends, vol. 2, no. 1, pp. 41-51, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S. Naresh Kumar, and B. Mohammed Ismail, “Systematic Investigation on Multi-Class Skin Cancer Categorization Using Machine Learning Approach,” Materials Today: Proceedings, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] MD Khairul Islam, Chetna Kaushal, and MD Al Amin, “Smart Home-Healthcare for Skin Lesions Classification with IoT Based Data Collection Device,” TechRxiv, IEEE Access, vol. 4, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Seena Joseph, and Oludayo O. Olugbara, “Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation,” Diagnostics, vol. 12, no. 2, pp. 344, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] D. Naveen Raju, Hariharan Shanmugasundaram, and D. Yuvaraj, “Hybrid Approach for Melonama Detection in Dermoscopic Images,” Materials Today: Proceedings, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Nithya Anoo et al., “An Efficient Skin Cancer Classification Approach Using Neural Networks,” Journal of Algebraic Statistics, vol. 13, no. 3, pp. 4946-4957, 2022.
[Google Scholar] [Publisher Link]
[8] Shubham Gupta, “An Anatomization for Classification Skin Lesion Using Custom CNN Framework,” International Conference on Industrial Electronics Research and Applications (ICIERA), pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jing Wu et al., “A Multi-Input CNNs with Attention for Skin Lesion Classification,” IEEE International Conference on Smart Cloud, pp. 78-83, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] T. Tamilselvi et al., “Deep Derma Scan: A Proactive Diagnosis System for Predicting Malignant Skin Tumor with Deep Learning Mechanisms,” International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 310-317, 2022.
[CrossRef] [Publisher Link]
[11] Yongwei Wang et al., “SSD-KD: A Self-Supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images,” Medical Image Analysis, vol. 84, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Sahib Khouloud et al., “W-Net and Inception Residual Network for Skin Lesion Segmentation and Classification,” Applied Intelligence, vol. 52, no. 4, pp. 3976-3994, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wessam Salma, and Ahmed S. Eltrass, “Automated Deep Learning Approach for Classification of Malignant Melanoma and Benign Skin Lesions,” Multimedia Tools and Applications, vol. 81, pp. 32643–32660, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Md. Mohsin Kabir et al., “CNN-NSVM Architecture for Skin Lesion Classification Using Non-Dermoscopic Digital Image,” Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] J. Ramya, H.C. Vijaylakshmi, and Huda Mirza Saifuddin, “Segmentation of Skin Lesion Images Using Discrete Wavelet Transform,” Biomedical Signal Processing and Control, vol. 69, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mohammed A. Al-masni, Dong-Hyun Kim, and Tae-Seong Kim, “Multiple Skin Lesions Diagnostics via Integrated Deep Convolutional Networks for Segmentation and Classification,” Computer Methods and Programs in Biomedicine, vol. 190, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Vedant Bhatt, and Mohammad Makki, “Artificial Intelligence for Curing Skin Disorders," SSRG International Journal of Computer Science and Engineering, vol. 5, no. 10, pp. 7-9, 2018.
[CrossRef] [Publisher Link]
[18] Anurhea Dutta et al., “Hybrid AES-DES Block Cipher: Implementation using Xilinx ISE 9.1 i.,” International Conference on Advances in Electronics and Electrical, pp. 1-5, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mina Javanmard Goldanloo, and Farhad Soleimanian Gharehchopogh, “A hybrid OBL-based Firefly Algorithm with Symbiotic Organisms Search Algorithm for Solving Continuous Optimization Problems,” The Journal of Supercomputing, vol. 78, no. 3, pp. 3998-4031, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yading Yuan, Ming Chao, and Yeh-Chi Lo, “Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance,” IEEE Transactions on Medical Imaging, vol. 36, no. 9, pp. 1876-1886, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jing Qian et al., “Prediction of MGMT Status for Glioblastoma Patients Using Radiomics Feature Extraction from 18F-DOPA-PET Imaging,” International Journal of Radiation Oncology* Biology* Physics, vol. 108, no. 5, pp. 1339-1346, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Jie Luo et al., “A New Kernel Extreme Learning Machine Framework for Somatization Disorder Diagnosis,” IEEE Access, vol. 7, pp. 45512-45525, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Yun Hou et al., “A Multi-Objective Discrete Particle Swarm Optimization Method for Particle Routing in Distributed Particle Filters,” Knowledge-Based Systems, vol. 240, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] ISIC Challenge Datasets. [Online]. Available: https://challenge.isic-archive.com/data/
[25] U. Padmavathi, and Narendran Rajagopalan, “Blockchain Enabled Emperor Penguin Optimizer Based Encryption Technique for Secure Image Management System,” Wireless Personal Communications, vol. 127, pp. 2347-2364, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Walaa Gouda et al., “Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning,” Healthcare, vol. 10, no. 7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Soumya Gadag, and P. Pradeepa, “A Critical Literature Review on Computer Vision Based Melanoma Detection and Identification,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 59-80, 2022.
[CrossRef] [Publisher Link]
[28] Vipul M Dabhi et al., “Detection and Classification of Skin Cancer using Back Propagated Artificial Neural Networks,” JES-Journal of Engineering Sciences, vol. 12, no. 6, pp. 686-693, 2021.
[Google Scholar] [Publisher Link]
[29] Jei Young Lee, “A Decentralized Token Economy: How Blockchain and Cryptocurrency Can Revolutionize Business,” Business Horizons, vol. 62, no. 6, pp. 773-784, 2019.
[CrossRef] [Google Scholar] [Publisher Link]