## 4 Conclusions  
Routine imaging examinations, such as mammographic image and breast ultrasound imaging, can be used to early detect breast lesions for increased survival rates and then can help save lives. Mammography and breast ultrasound are both first-line manners for performing clinical examinations. However, breast ultrasound imaging has a poor screening capacity for small calcifications detection (as the earliest signs of breast cancer)and is required to combine with diagnostic mammography to evaluate the suspected breast lesions and changes in breast tissues. A breast ultrasound is an assistive tool for screening breast cancer and offers a visual guide for performing a biopsy. For a follow-up screening, with low-dose X-rays to view the breast tissue, where an abnormal screening mammogram is obtained, the clinicians or radiologists can capture more images to inspect suspicious lesions, such as calcifications or small tumors, as the earliest signs of breast cancer. Hence, based on mammographic image classification, the proposed 2D spatial and 1D CNN-based classifier could be directly used to screen breast lesions in clinical applications. In contrast to the ML- and DL-based methods [24; 25; 26; 27; 28; 29; 37; 38; 59; 60; 61], the proposed multilayer classifier had some advantages: (1) spatial convolutional process for enhancing the breast lesions' features; (2) two-round 1D convolutional processes for identifying the differences between normal and B/M classes; and (3) straightforward mathematic operations for performing the training and screening tasks. Through 10-fold cross-validation tests, we obtained promising results for screening breast lesions, with a high classifier mean F1 score (0.9641), precision (96.70%), and recall (96.13%) for separating the Nor from the B and M classes. However, with the mammographic images, women had background tissue type (especially in Asians), such as higher breast density, which might affect the classification accuracy in mammographic images [38]. As lesions might be shadowed by dense tissues, such as dense breast or intermediate mixed-type breast density, AI-based methods might not identifyaccurately at the early stage, thus increasing patients' risk of developing breast cancer. The proposed screening model has overcome limitations, such as parameters assignment, parameters adjustment, iteration computation, and optimization algorithm requirements. Its training scheme has an adaptive capability to retrain the classifier with new image datasets in less computation time, such as clinical images, the DDSM database, or the INbreast database. Hence, new/special mammographic images were continuously considered for addition to the training datasets, which could rapidly retrain the classifier and maintain its intended medical purpose. Its pattern recognition scheme could be carried out as a computer-aided decision-making tool or a software in a medical device (SaMD) tool [64; 65]. Therefore, we suggest the proposed automatic screening model could replace the traditional CNN methods for specific tasks requiring expertise and experience in medical image examinations, such as diagnostic mammogram, CT, and MRI, which will help to reduce the burden and to focus on follow-up decision making and medical strategies. Conceptualization: [PERSON]-[PERSON], [PERSON] and [PERSON]; analysis and materials: [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]; data analysis: [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft preparation: [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]; writing--review and editing: [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]; supervision: [PERSON], [PERSON] and [PERSON]; funding acquisition: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Science and Technology, Taiwan, under contract number: MOST 109-2635-E-167-001 and MOST 110-2221-E-167-033, duration: 1 August 2019-31 July 2022.