# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
###### Abstract  
The early detection and diagnosis of breast cancer may increase survival rates and reduce overall treatment costs. The cancer of the breast is a severe and potentially fatal disease that impacts individuals worldwide. Mammography is a widely utilized imaging technique for breast cancer surveillance and diagnosis. However, images produced with mammography frequently contain noise, poor contrast, and other anomalies that hinder radiologists from interpreting the images. This study develops a novel deep-learning technique for breast cancer detection using mammography images. The proposed procedure consists of two primary steps: region-of-interest (ROI) (1) extraction and (2) classification. At the beginning of the procedure, a YOLOX model is utilized to distinguish breast tissue from the background and to identify ROIs that may contain lesions. In the second phase, the EfficientNet or ConvNeXt model is applied to the data to identify benign or malignant ROIs. The proposed technique is validated using a large dataset of mammography images from various institutions and compared to several baseline methods. The pF1 index is used to measure the effectiveness of the technique, which aims to establish a balance between the number of false positives and false negatives, and is a harmonic mean of accuracy and recall. The proposed method outperformed existing methods by an average of 8.0%, obtaining superior levels of precision and sensitivity, and area under the receiver operating characteristics curve (ROC AUC) and the precision-recall curve (PR AUC). In addition, ablation research was conducted to investigate the effects of the procedure's numerous components. According to the findings, the proposed technique is another choice that could enhance the detection and diagnosis of breast cancer using mammography images. egro-of-interest optimization; breast cancer detection; mammography; YOLOX; EfficientNet; ConvNeXt +
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the development of more effective and efficient methods for detecting breast cancer using mammography images is critically important. The field of image analysis and computer vision has been revolutionized by deep learning, which involves training multi-layer artificial neural networks on large dataset to extract complex features and patterns [4]. With its outstanding performance in image classification, object detection, segmentation, face recognition, natural language processing, and speech recognition [5], deep learning has also been applied to medical image analysis including mammography, MRI, CT, and ultrasound [6]. Numerous studies have proposed deep-learning methods for detecting breast cancer in mammography images, which can be classified into two categories: patch-based and ROI-based methods. Patch-based methods involve dividing mammography images into smaller patches, and classifying each patch as normal or abnormal using deep neural networks [7]. ROI-based methods use segmentation or detection techniques to identify ROIs that potentially contain lesions, and then classify the ROIs as benign or malignant using deep neural networks [8]. Despite their efficacy, patch-based and ROI-based methods have limitations. Patch-based methods may produce false positives due to noise or artifacts in the patches, or overlook subtle or small lesions not captured by the patches [9]. ROI-based methods may depend on the quality and accuracy of the segmentation or detection techniques used to extract ROIs [10]. Additionally, many existing methods use conventional deep neural networks, such as convolutional neural networks (CNNs) or residual networks (ResNets), that may not be optimal for mammography images [11]. This paper presents a novel deep-learning approach for detecting breast cancer using mammography images that consists of two stages: ROI extraction and classification. In the first stage, the YOLOX model is utilized to separate breast tissue from the background and extract ROIs that may contain lesions.