A Medical Image Classification Model based on Quantum-Inspired Genetic Algorithm

Authors

  • Hussain K. Ibrahim National School of Electronics and Telecommunications (ENET’COM) of Sfax, University of Sfax, Tunisia | High Institute of Applied Science Technology of Sousse, University of Sousse, Tunisia | Research Groups in Intelligent Machines (REGIM Laboratory), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia | College of Computer Science and Information Technology, Wasit University, Iraq
  • Nizar Rokbani High Institute of Applied Sciences and Technology of Sousse University of Sousse, Tunisia | Research Groups in Intelligent Machines (REGIM Laboratory), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia | Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942 Saudi Arabia
  • Ali Wali Higher Institute of Computer Science and Multimedia of Sfax, University of Sfax, Tunisia | Research Groups in Intelligent Machines (REGIM Laboratory), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia
  • Khmaies Ouahada Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park, South Africa
  • Habib Chabchoub College of Business, Al Ain University of Science and Technology, Abu Dhabi, United Arab Emirates
  • Adel M. Alimi Research Groups in Intelligent Machines (REGIM Laboratory), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia | Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa
Volume: 14 | Issue: 5 | Pages: 16692-16700 | October 2024 | https://doi.org/10.48084/etasr.8430

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 neighbors

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Author Biographies

Ali Wali, Higher Institute of Computer Science and Multimedia of Sfax, University of Sfax, Tunisia | Research Groups in Intelligent Machines (REGIM Laboratory), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia

 

 

Khmaies Ouahada, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park, South Africa

 

 

Habib Chabchoub, College of Business, Al Ain University of Science and Technology, Abu Dhabi, United Arab Emirates

 

 

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How to Cite

[1]
Ibrahim, H.K., Rokbani, N., Wali, A., Ouahada, K., Chabchoub, H. and Alimi, A.M. 2024. A Medical Image Classification Model based on Quantum-Inspired Genetic Algorithm. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 16692–16700. DOI:https://doi.org/10.48084/etasr.8430.

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