International Journal of Electrical and Computer Engineering (IJECE) Vol. 15, No. 2, April 2025, pp. 1670~1685 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i2.pp1670-1685  1670 Journal homepage: http://ijece.iaescore.com A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification Vinoth Rathinam 1 , Sasireka Rajendran 2 , Valarmathi Krishnasamy 1 1 Department of Electronics and Communication Engineering, P.S.R. Engineering College, Tamilnadu, India 2 Department of Biotechnology, Mepco Schlenk Engineering College, Tamilnadu, India Article Info ABSTRACT Article history: Received Jul 15, 2024 Revised Nov 30, 2024 Accepted Dec 14, 2024 A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care. Keywords: Breast cancer detection Contrast limited adaptive histogram equalization Deep learning, and computed aided diagnosis Gated attention deep convolution network classification Lichtenberg optimization algorithm YOLO-based attention network segmentation This is an open access article under the CC BY-SA license. Corresponding Author: Vinoth Rathinam Department of Electronics and Communication Engineering, P.S.R. Engineering College Sivakasi, Tamilnadu, India Email: vinoth@psr.edu.in 1. INTRODUCTION According to the World Health Organization (WHO) report, breast cancer [1], [2] is regarded as the second biggest cause of morbidity for women, and nearly 8.2 million people die each year from cancer and predicts the statistic will grow to 27 million by 2030 [3]. Therefore, early diagnosis, timely and accurate detection, and proactive prevention are essential elements in lowering the mortality rate for women [4], [5]. Further, it is essential to predict the disease at an early stage so that the treatment can be given well in advance. For locating and precisely identifying the tumor-affected area, many imaging modalities are used. Medical professionals often use mammography images in tandem with other imaging methods to diagnose and treat patients with accuracy [6]–[8]. Various medical image processing techniques have been employed in the existing research with the intent of detecting breast cancers from mammograms [9], [10]. Computer- aided diagnosis (CAD) [10], [11] systems require the detection, segmentation, and classification of medical