# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
Accordingly, in recent years, artificial intelligence (AI) methods and the collection of data for big data (BD) are playing increasingly important roles for automatic tumor screening, such as liver, lung, and breast cancers. AI methods include models based on machine learning, deep learning, and broad learning [2; 3; 4; 5; 6; 7; 8; 9]. For example, in Taiwan, data collection for BD includes medical images of relevant major diseases that major medical centers have been actively collecting both locally and abroad in recent years (since 2018). These medical images comprise fifteen categories, including X-ray images, angiography, magnetic resonance imaging (MRI), and computer tomography (CT). Additionally, a database such as CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography), is a database containing approximately 2500 enrolled subjects, employed for the studies of the mammographic classification of breast lesions. This classification consists of normal cases, benign cases, malignant cases, and pathology information [10], which provides ground truth validation that makes the DDSM applicable in the development and validation of the decision support systems. The region of interest (ROI) and pathological information can be gathered continuously in the current database. Coupled with the improvement of information communication, digital data processing, AI methods, and machine vision models, software and hardware equipment can process the large amount of digital data [11; 12; 13]. However, a decision support system with computer-aided diagnosis and detection algorithms for breast tumor screening in mammography are needed. Currently, regarding AI algorithms, the multilayer machine vision recognition model that is composed of convolutional neural networks (CNNs) is the most commonly used model. It can be used for digital image processing, such as feature enhancement, feature extraction, data simplification, and pattern recognition task [2; 3; 4; 5; 6; 7; 8; 9; 13; 14; 15]. Hence, this study uses breast mammogram images to establish a two-dimensional (2D) fractional order-based CNN classifier that can automatically perform breast tumor screening tasks in clinical applications. We expect that a set of automatic screening tools suitable for clinical usage that takes in mammogram images can be developed to achieve the rapid identification of whether a tumor is malignant or benign. When any breast tumor is suspected, the results of the rapid screening can be used as a basis of reference for subsequent fine needle aspiration cytology/biopsy examinations. Hence, this assistive tool solves the problem of insufficient human resources for manual screening, which can potentially lead to additional problems in the medical diagnosis process. Solving this procedural congestion allows clinicians to focus more on follow-up medical strategies. The 2D CNNs may comprise several convolutional-pooling layers and a fully connected network in the classification layer, such as back-propagation neural networks and Bayesian networks, which combine the image enhancement, feature extraction, and classification tasks to form an individual scheme [16; 17] which achieves promising accuracy for image classification in breast tumor screening. These CNNs are usually greater than 10 convolutional-pooling layers which perform the above mentioned image preprocessing and postprocessing tasks and then increase the identification accuracy. Hence, this multilayer design may gradually replace machine-learning (ML) methods [18; 19], which perform image segmentation and feature extraction as an image preprocess for mammograms and breast MRIs and then use the fixed features obtained to train a classifier. Both CNN and ML-based image segmentation [20] can learn the specific features or knowledge representations to automatically identify the boundaries of ROI and then detect the breast lesions.