###### Abstract  
Breast cancer profoundly affects women's lives; its early diagnosis and treatment increase patient survival chances. Mammography is a common screening method for breast cancer, and many methods have been proposed for automatic diagnosis. However, most of them focus on single-label classification and do not provide a comprehensive analysis concerning density, abnormality, and severity levels. We propose a method based on the multi-label classification of two-view mammography images to comprehensively diagnose a patient's condition. It leverages the correlation between density type, lesion type, and states of lesions, which radiologists usually perform. It simultaneously classifies mammograms into the corresponding density, abnormality type, and severity level. It takes two-view mammograms (with craniocaudal and mediolateral oblique views) as input, analyzes them using ConvNeXt and the channel attention mechanism, and integrates the information from the two views. Finally, the fused information is passed to task-specific multi-branches, which learn task-specific representations and predict the relevant state. The system was trained, validated, and tested using two public domain benchmark datasets, INBresat and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), and achieved state-of-the-art results. The proposed computer-aided diagnosis (CAD) system provides a holistic observation of a patient's condition. It gives the radiologists a comprehensive analysis of the mammograms to prepare a full report of the patient's condition, thereby increasing the diagnostic precision. breast cancer; mammography; deep learning; multi-label classification; convolutional neural network (CNN) +
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two views are more helpful in improving a diagnosis; however, this method requires special treatment to avoid redundant data existing in both views. Therefore, using a fusion technique helps to handle this case. In addition, most studies model the diagnosis problem as a single-label classification, such as detecting the breast density type [7; 8; 9; 10], identifying the masses as benign or malignant [11; 12; 13; 14], diagnosing microcalcifications as benign or malignant [15; 16], or classifying both masses and calcifications as benign or malignant [17; 18]. Single-label classification ignores the interdependencies between different conditions; for instance, a breast with high density is more likely to be malignant than a breast with low density. In addition, single-label classification requires developing multiple methods, each focusing on one aspect of the problem. These issues can be overcome by using a method based on multi-label classification for identifying breast cancer in an initial phase. A method that formulates the diagnosis as a multi-label classification problem can help to diagnose a patient's condition comprehensively. It leads to better diagnosis results by considering additional aspects, such as the correlation between the density type, lesion type, and states of the lesions, which radiologists usually perform. It can assist radiologists in their decision making by providing a comprehensive report of the patient's condition, increasing the precision of their diagnosis. Given the above discussion, we formulate a diagnosis as a multi-label classification using two views. Inspired by the success of advanced CNN and transformer models [19; 20; 21; 22; 23; 24; 25], we designed the proposed method using four modules: a feature extraction module, an attention module, a fusion module, and a multi-label classification module. First, the feature extraction module employs the state-of-the-art CNN and transformer models, such as the Swin transformer and ConvNeXt, and extracts features from two views, CC and MLO. The attention module concentrates on relevant features and suppresses irrelevant features. The information extracted from the two views is fused using the fusion module.