International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 5, October 2022, pp. 5001~5013 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp5001-5013  5001 Journal homepage: http://ijece.iaescore.com Optimized textural features for mass classification in digital mammography using a weighted average gravitational search algorithm Oludare Yinka Ogundepo 1 , Isaac Ozovehe Avazi Omeiza 2 , Jonathan Ponmile Oguntoye 3 1 Department of Electrical and Electronics Engineering, College of Technology, Federal University of Petroleum Resources, Effurun, Nigeria 2 Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria 3 Department of Computer Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso, Nigeria Article Info ABSTRACT Article history: Received Apr 18, 2021 Revised Feb 3, 2022 Accepted Apr 6, 2022 Early detection of breast cancer cells can be predicted through a precise feature extraction technique that can produce efficient features. The application of Gabor filters, gray level co-occurrence matrices (GLCM) and other textural feature extraction techniques have proven to achieve promising results but were often characterized by a high false-positive rate (FPR) and false-negative rate (FNR) with high computational complexities. This study optimized textural features for mass classification in digital mammography using the weighted average gravitational search algorithm (WA-GSA). The Gabor and GLCM features were fused and optimized using WA-GSA to overcome the weakness of the textural feature techniques. With support vector machine (SVM) used as the classifier, the proposed algorithm was compared with commonly applied techniques. Experimental results show that the SVM with WA-GSA features achieved FPR, FNR and accuracy of 1.60%, 9.68% and 95.71% at 271.83 s, respectively. Meanwhile, SVM with Gabor features achieved FPR, FNR and accuracy of 3.21%, 12.90% and 93.57% at 2351.29 s, respectively, while SVM with GLCM features achieved FPR, FNR and accuracy of 4.28%, 18.28% and 91.07% at 384.54 s, respectively. The obtained results show the prevalence of the proposed algorithm, WA-GSA, in the classification of breast cancer tumor detection. Keywords: Breast cancer Digital mammography Gabor filter Gravitational search algorithm Gray level co-occurrence matrices Support vector machine Texture features This is an open access article under the CC BY-SA license. Corresponding Author: Oludare Yinka Ogundepo Department of Electrical and Electronics Engineering, College of Technology, Federal University of Petroleum Resources Effurun, Delta State, Nigeria Email: oludare.ogundepo@fupre.edu.ng 1. INTRODUCTION Digital mammography is a powerful technique that helps in the diagnosis of breast cancers at premature stages [1]. The early detection of breast cancer helps prevent the growth to a complicated stage which could lead to the need for surgeries. This forestalls unnecessary biopsies and radiation therapies by proper screening and abnormality detection; thus, increases the likelihood of patient’s survival [2], [3]. The malignancy can be found in patients in the presence of masses and microcalcifications in the breast region. The successful analysis of breast cancer relies on features extracted from the cancer suspicious areas and classification of the features using a classifier or the combinations of classifiers [4], [5]. The enrichment and