A Medical Image Classification Model based on Quantum-Inspired Genetic Algorithm
Received: 18 July 2024 | Revised: 1 August 2024 | Accepted: 4 August 2024 | Online: 9 October 2024
Corresponding author: Hussain K. Ibrahim
Abstract
This study used a Quantum-Inspired Genetic Algorithm (QIGA) to select the proper functionality and reduce the dimensions, classification time, and computational cost of a learning dataset. QIGA reduces the complexity of solutions and improves the selection of the best features. The application of quantum principles, in particular the unpredictability of quantum chromosomes, which are represented by qubits, can help in investigating a significantly more extensive solution space. QIGA offers a novel approach to feature selection in optimization problems. Using principles from quantum computing, this algorithm aims to enhance the efficiency and effectiveness of the feature selection process to increase performance. This indicates that features of both exploration and exploitation are embodied by QIGA without requiring massive amounts of data. Considerable gains in classification accuracy were achieved compared to traditional methods. The dynamic design of the models through the evolutionary mechanism in QIGA enables the optimization process to adapt to varying probabilities produced from the qubit overlay via the quantum rotation gate. This is contrary to traditional methods. The model using QIGA offered a more precise classification than the model optimized by Genetic Algorithms (GA). The proposed method achieved superior performance in terms of classification accuracy, with a score of more than 98%, compared to GA, which achieved a classification accuracy of 94%.
Keywords:
medical image classification, deep learning, machine learning, genetic algorithm, quantum-inspired, K-nearest neighborsDownloads
References
Q. Zhang, L. T. Yang, Z. Chen, and P. Li, "A survey on deep learning for big data," Information Fusion, vol. 42, pp. 146–157, Jul. 2018.
R. A. Hasan, M. F. Alomari, and J. B. Jamaluddin, "Comparative study: Using machine learning techniques about rainfall prediction," AIP Conference Proceedings, vol. 2787, no. 1, Jul. 2023, Art. no. 050014.
F. Alam Khan, M. Asif, A. Ahmad, M. Alharbi, and H. Aljuaid, "Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development," Sustainable Cities and Society, vol. 55, Apr. 2020, Art. no. 102018.
F. Alam Khan, M. Asif, A. Ahmad, M. Alharbi, and H. Aljuaid, "Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development," Sustainable Cities and Society, vol. 55, Apr. 2020, Art. no. 102018.
A. Aboud et al., "A Distributed Multifactorial Particle Swarm Optimization Approach." TechRxiv.
I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635–640, Jun. 2020.
S. M. Ayyoubzadeh, S. M. Ayyoubzadeh, H. Zahedi, M. Ahmadi, and S. R. N. Kalhori, "Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study," JMIR Public Health and Surveillance, vol. 6, no. 2, Apr. 2020, Art. no. e18828.
L. Sun et al., "Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT," IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2798–2805, Jul. 2020.
Z. Laboudi and S. Chikhi, "Comparison of Genetic Algorithm and Quantum Genetic Algorithm," The International Arab Journal of Information Technology, vol. 9, no. 3, pp. 243–249, 2012.
R. H. Ali and W. H. Abdulsalam, "The Prediction of COVID 19 Disease Using Feature Selection Techniques," Journal of Physics: Conference Series, vol. 1879, no. 2, Feb. 2021, Art. no. 022083.
A. Aboud, N. Rokbani, S. Mirjalili, A. Hussain, H. Chabchoub, and A. M. Alimi, "A Quantum Beta Distributed Multi-Objective Particle Swarm Optimization Algorithm for Twitter Fake Accounts Detection." TechRxiv, Jul. 14, 2023.
Y. Soussi, N. Rokbani, M. M. B. Khelifa, A. Wali, and N. T. Phuong, "Clustering multi-objectifs basée sur l’algorithme d’essaim de salpedia bêta- distribués (Multi-Objectif Beta Salp Swarm Algorithm MO-β-SSA)," Laboratoire LIS, Carqueiranne, France, May 2023.
C. Mair et al., "An investigation of machine learning based prediction systems," Journal of Systems and Software, vol. 53, no. 1, pp. 23–29, Jul. 2000.
A. A. Abdulhussien, M. F. Nasrudin, S. M. Darwish, and Z. Abdi Alkareem Alyasseri, "Feature selection method based on quantum inspired genetic algorithm for Arabic signature verification," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 3, pp. 141–156, Mar. 2023.
N. Zeng, Z. Wang, W. Liu, H. Zhang, K. Hone, and X. Liu, "A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm," IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9290–9301, Sep. 2022.
A. R. Lubis, M. Lubis, and A. Khowarizmi, "Optimization of distance formula in K-Nearest Neighbor method," Bulletin of Electrical Engineering and Informatics, vol. 9, no. 1, pp. 326–338, Feb. 2020.
A. A. Abdulhussien, M. F. Nasrudin, S. M. Darwish, and Z. Abdi Alkareem Alyasseri, "Feature selection method based on quantum inspired genetic algorithm for Arabic signature verification," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 3, pp. 141–156, Mar. 2023.
S. M. Darwish, I. A. Mhaimeed, and A. A. Elzoghabi, "A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications," Entropy, vol. 25, no. 9, Sep. 2023, Art. no. 1271.
M. Khanna, A. Agarwal, L. K. Singh, S. Thawkar, A. Khanna, and D. Gupta, "Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images," Arabian Journal for Science and Engineering, vol. 48, no. 8, pp. 11051–11083, Aug. 2023.
T. T. Nguyen, N. Q. Luc, and T. T. Dao, "Developing Secure Messaging Software using Post-Quantum Cryptography," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12440–12445, Dec. 2023.
A. H. Alaidi, C. S. Der, and Y. W. Leong, "Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9732–9736, Dec. 2022.
A. H. Alaidi, S. D. Chen, and Υ. W. Leong, "Artificial Bee Colony with Crossover Operations for Discrete Problems," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9510–9514, Dec. 2022.
H. Zhang, H. Xu, X. Tian, J. Jiang, and J. Ma, "Image fusion meets deep learning: A survey and perspective," Information Fusion, vol. 76, pp. 323–336, Dec. 2021.
K. T. Powers and J. D. Santoro, "Metabolic stroke-like episode in a child with FARS2 mutation and SARS-CoV-2 positive cerebrospinal fluid," Molecular Genetics and Metabolism Reports, vol. 27, Jun. 2021, Art. no. 100756.
T. Tuncer, "Fusion and Deep Learning," Computers, Materials & Continua, vol. 64, 2021, Art. no. 102257.
T. Rahman, M. Chowdhury, and A. Khandakar, "COVID-19 Radiography Database." [Online]. Available: https://www.kaggle.com/
datasets/tawsifurrahman/covid19-radiography-database.
V. Madaan et al., "XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks," New Generation Computing, vol. 39, no. 3, pp. 583–597, Nov. 2021.
M. Umer, I. Ashraf, S. Ullah, A. Mehmood, and G. S. Choi, "COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images," Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 1, pp. 535–547, Jan. 2022.
S. Albahli and G. N. A. H. Yar, "Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study," Journal of Medical Internet Research, vol. 23, no. 2, Feb. 2021, Art. no. e23693.
H. K. Ibrahim, N. Rokbani, A. Wali, and A. M. Alimi, "GA-NN and PSO-NN for Medical Images Classification: A Comparative Analysis," in 2023 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE), Sousse, Tunisia, Oct. 2023, pp. 1–6.
M. Chavan, V. Varadarajan, S. Gite, and K. Kotecha, "Deep Neural Network for Lung Image Segmentation on Chest X-ray," Technologies, vol. 10, no. 5, Oct. 2022, Art. no. 105.
S. Mathesul et al., "COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques," Algorithms, vol. 16, no. 10, Oct. 2023, Art. no. 494.
Downloads
How to Cite
License
Copyright (c) 2024 Hussain K. Ibrahim, Nizar Rokbani, Ali Wali, Khmaies Ouahada, Habib Chabchoub, Adel M. Alimi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.