###### Abstract  
Mammography is a first-line imaging examination that employs low-dose X-rays to rapidly screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) spatial and one-dimensional (1D) convolutional neural network (CNN) to early detect possible breast lesions (tumors) to reduce patients' mortality rates and to develop a classifier for use in mammographic images on regions of interest where breast lesions (tumors) may likely occur. The 2D spatial fractional-order convolutional processes are used to strengthen and sharpen the lesions' features, denoise, and improve the feature extraction processes. Then, an automatic extraction task is performed using a specific bounding box to sequentially pick out feature patterns from each mammographic image. The multi-round 1D kernel convolutional processes can also strengthen and denoise 1D feature signals and assist in the identification of the differentiation levels of normality and abnormality signals. In the classification layer, a gray relational analysis-based classifier is used to screen the possible lesions, including normal (Nor), benign (B), and malignant (M) classes. The classifier development for clinical applications can reduce classifier's training time, computational complexity level, computational time, and achieve a more accurate rate for meeting clinical/medical purpose. Mammographic images were selected from the mammographic image analysis society image database for experimental tests on breast lesions screening and K-fold cross-validations were performed. The experimental results showed promising performance in quantifying the classifier's outcome for medical purpose evaluation in terms of recall (%), precision (%), accuracy (%), and F1 score. m 2022 25 July 2022 25 July 2022 26 July 2022 2  
(ML)-based and deep learning (DL)-based methods [3; 4; 5], for detecting suspicious regions or any breast lesions in the early stage  
Breast abnormalities may appear as different shapes and tissue characteristics in mammography examination, such as mass (lesion), architectural distortion, calcification, and asymmetry, which indicate whether a mass is a benign or malignant tumor [6; 7; 8]. In clinical image examinations, morphological features indicate important information in diagnosing breast lesions. The bilateral view is a routine manner for clinical screening mammography, which can increase accuracy with multi-view projections. For a mass lesion, if a mass is seen in a single projection, the so-called asymmetry, and then shape, margin, and density are confirmed to identify the lesion category. Its shape is round and oval masses for benign categories, and lobular, or irregular shapes for malignant categories. The density can be high, low, or fat-containing, which is related to the lesion categories, as high density is for malignant and fat-containing is for lesions that are typically benign, or calcification. Calcification in mammograms can be diffuse, regional, cluster, linear, or segmental and has two types: macrocalcification and microcalcification. Breast lesions are heterogeneous diseases with different characteristic features in both benign and malignant lesions. Hence, some studies [6; 9; 10; 11; 12] extract feature patterns with a specific bounding box from suspicious mammographic images as some templates of feature patterns, including normal (Nor), benign (B), and malignant (M), as shown in Figure 1. They use morphological features to identify the normality or abnormality for automatic breast tumor screening. ML- and DL-based methods, such as image enhancement, image segmentation, feature extraction, and image classification, have been applied for breast tumor screening to increase speed in detection tasks and also to decrease humans' manual errors. Hence, using AI-based methods can help clinicians or radiologists to easily detect the breast lesions and to make better diagnostic decisions for treatment plans.