Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3687 2019 2019 Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; Department of Mathematics, Faculty of Science, Sohag University, 82524 Sohag, Egypt S. S. Breast cancer (BC) is one of the most common invasive cancers, which cause thousands of women's deaths globally. Therefore, prompt detection is a cure for reducing the rate of death. Therefore, screening of BC in its initial phase is of utmost vital. Physically segmenting breast lesion imaging appears a time-consuming and expensive pursuit for radiologists. Hence, the adoption of automatic analytic techniques becomes vital, directing to exactly segment lesions of the breast and mitigate the associated tasks. The segmentation of malignant areas is an essential procedure in the complete inspection of breast image data. To achieve the segmentation and recognition of BC, numerous computer-aided diagnosis (CAD) techniques were presented for the investigation of mammogram imaging. The CAD models are employed to mainly analyze the disorder and provide the best treatment. Currently, deep learning (DL) techniques are superior and provide promising results in the early recognition of BC. In this paper, we design a Leveraging Quantum Algorithms for Edge Detection in Mammograms to Improve Breast Cancer Screening (LQAEDM-IBCS) model. The main intention of the LQAEDM-IBCS is to provide an accurate and effective technique for the detection and segmentation process of breast cancer using advanced algorithms. Initially, the image pre-processing stage applies the adaptive bilateral filtering (ABF) method to eliminate the unwanted noise in input image data.  Next, the segmentation process is implemented by the Otsu threshold method for edge detection. To improve the segmentation performance, the parameter tuning process is performed through the quantum spotted hyena optimizer (QSHO) algorithm. Besides, the proposed LQAEDM-IBCS technique designs the DenseNet-121 method for the extraction of feature procedure. Eventually, the quantum neural network (QNN) method has been deployed for the BC classification process. The simulating validation of the LQAEDM-IBCS system is verified on a benchmark image database and the outcomes are dignified under numerous measures. The experimental outcome emphasized the enlargement of the LQAEDM-IBCS approach in the BC diagnosis process. 2025 2025 68 81 10.54216/JISIoT.160206 https://www.americaspg.com/articleinfo/18/show/3687