Towards early breast cancer detection using equilibrium optimizer algorithm

Moradi, Mohammad; Rezai, Abdalhossein; Sadollah, Ali

doi:10.47974/JIOS-1939

Open Access Research Article

Towards early breast cancer detection using equilibrium optimizer algorithm

Mohammad MoradiMohamadmoradi0078@gmail.comDepartment of Electrical and Biomedical Engineering ACECR Institute of Higher Education Isfahan BranchIsfahan, 8418155759, Iran0009-0007-0310-8999View full profile → , ^*Abdalhossein RezaiCorresponding authorrezai@usc.ac.irDepartment of Electrical Engineering Faculty of Engineering University of Science and CultureTehran, 1461968151, Iran0000-0001-8529-499XView full profile → , Ali Sadollahsadollah@usc.ac.irDepartment of Mechanical Engineering Faculty of Engineering University of Science and CultureTehran, 1461968151, Iran0000-0002-7782-4126View full profile →

* Corresponding author · click or hover a name for details

pp. 1–16Online FirstMarch 2026DOI: 10.47974/JIOS-1939XML

Received:: 01 Aug 2024
Published Online:: 20 Mar 2026
Article type:: Research Article
Language:: EN
Article no.:: JIOS-1939
Pages:: 1–16

Abstract

Breast cancer has an important role in mortality among women. Early and accurate diagnosis is essential for effective treatment. The Computer-Aided Detection (CAD) system can assist healthcare professionals in the diagnostic process. This research introduces a practical approach to enhance the CAD system’s performance in breast cancer diagnosis by analyzing thermal images. The research methodology in the suggested CAD system involves the integration of efficient algorithms during the feature extraction and classification stages, along with a novel feature selection algorithm. The SFTA technique is employed for feature extraction. Then, the EO algorithm is applied to the extracted features. The SVM and DTree classification techniques are then implemented to assess the discriminative efficacy of the selected features, aiming for successful group classification. The suggested method is evaluated using the DMR database. The results demonstrate an accuracy of 98.5%, specificity of 99%, and sensitivity of 98%. The breast cancer diagnostic method that was developed exhibits advantages over other approaches that utilize thermography.

Keywords

Breast cancer Equilibrium-optimizer algorithm Feature selection Thermography Computer-aided detection system Feature extraction

Subject Classifications

68U1054H3092C55

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