## 1 Introduction  
Despite substantial technological advancements, cancer detection at an initial stage remains a challenging one, and present cancer detection methods are time-consuming, expensive, complex, and uncomfortable [1]. Growths in organic electronic materials with optical imaging modalities, improvised models of different optical properties, optical biosensors, and biocompatibility are auspicious approaches for initial cancer detection and that of other diseases. Latest imaging techniques are compiled with the progression of ultrasound, positron emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI) for enhanced diagnosis of cancer and treatment which will help screen patients more accurately [2]. Developments in optical biosensors or organic electronics help in the segregation between healthy and cancer cells and the remodeling point-of-care gadgets can be used in cancer diagnosis [3]. Presently, utilizing ultrasound approaches for cancer detection depends on the experience of the clinician, particularly for the measurements and marks of cancers. To be specific, a clinician generally makes use of ultrasound instruments for cancer recognition by identifying a good angle for a clear vision of cancer displayed on the screen, and after continuing investigation fixing this for a longer period by utilizing one hand, with the other hand marking and measuring the cancers on the screen [4; 5]. It becomes a tough task, because slight shaking of the hand which holds the probe may have a significant influence on the quality of breast ultrasound imagery; depending on this, computer-aided automated detection technologies are in high demand to locate regions of interest (ROIs), i.e., cancers, in breast ultrasound images [6].  
Many researchers have involved computer-aided diagnosis (CAD) methods for breast cancer detection; such methods invoke the usage of Bayesian networks, artificial neural networks (ANN), k-means clustering, decision trees (DT), and fuzzy logic (FL) [7; 8]. However, some of the authors have applied CAD techniques with DOT for diagnosing breast cancer. In recent times, convolutional neural networks (CNNs) have proved to be effective in distinguishing between malignant and benign breast lesions [9]. In comparison with classical techniques, CNNs eliminate the stages involved in feature extracting of an image; on the other hand, they provide images straight to the network which could automatically study discriminatory features [10]. CNN architecture was specifically adapted to have the benefit of 2D structures for input images.  
This article introduces an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN method was utilized for allocating suitable class labels to the input images. Finally, the AO technique can be employed to fine-tune the parameters relevant to the BNN method so that the classification performance will be enhanced, showing the novelty of our work. To validate the enriched performance of the AOBNN-BDNN algorithm, a wide experimental study is executed on a benchmark dataset.  
The rest of the paper is organized as follows. Section 2 offers a brief survey of breast cancer classification using ultrasound images. Section 3 elaborates on the proposed model and Section 4 validates the performance of the proposed model. Lastly, Section 5 concludes the study.