# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 3 Experimental Results
### Discussion  
This study designs a mammography classification method incorporating a multilayer CNN-based classifier for automatic breast tumor screening in clinical applications. The proposed classifier algorithm is implemented in the LabVIEW 2019 (N1 TM) software, MATLAB Script tools, and open-source Tensorflow platform (Version 1.9.0) [28] and integrated into a computer assistive system with the automatic and manual feature extraction and breast tumor screening modes. The fractional-order convolutional layer and two convolutional-pooling layers allow the image enhancement and sharpening of the possible tumor edges, contours, and shapes via one fractional-order and two kernel convolutional processes in the feature patterns. Through a series of convolution and pooling processes at different scales and different dimensions, the classifier can obtain nonlinearity feature representation from low-level features to high-level information [29]. Then, with the specific bounding  
Figure 8: Human–machine interface of the computer assistive system and its automatic screening results. boxes (automatic or manual mode) for ROI extraction, enhanced feature patterns can then be distinguished for further breast tumor screening by the multilayer classifier in the classification layer. A gradient-descent optimization method, namely, the ADAM algorithm, is used in the back-propagation process to adjust the network weighted parameters in the classification layer. With K-fold (\(K_{f}=10\)) cross-validation, the 466 randomly selected untrained feature patterns for each test fold, the proposed multilayer CNN-based classifier, has high recall (%), precision (%), accuracy (%), and F1 scores for screening abnormalities in both right and left breasts. Experimental results show that the proposed multilayer CNN model offers image enhancement, feature extraction, automatic screening capability, and higher average accuracy (larger than 95%) for separating the normal condition from the possible tumor classes. It has been observed from previous literature [3; 4; 56; 6; 7; 10; 57] that multilayer CNNs comprised several convolutional-pooling layers and a fully connecting network to establish a classifier for automatic breast tumor screening, and could also be applied for CT, MRI, chest X-ray, and ultrasound image processes, such as image classification and segmentation in clinical applications [19; 23; 28; 35; 36; 51; 55]. The combination of a cascade of deep learning and a fully connecting networks is also carried out by a multilayer CNN-based classifier, and a decision scheme [57]. For the screened suspicious region on mammograms, the cascade of the deep-learning method had 98% sensitivity and 90% specificity on the SuReMapp (Suspicious Region Detection on Mammogram from PP) dataset [57], and 94% sensitivity and 91% specificity on the mini-MIAS dataset [57]. This CNN-based multilayer classifier could extract multi-scale feature patterns, and increase the depth and width feature patterns by using multi-convolutional-pooling processes, which had an overall increase in accuracy. However, excessive multi-convolutional processes would completely lead to a loss of the internal data about the position and the orientation of the desired object, and an excessive multi-pooling processing would lose valuable information relating to the spatial relationships between features; thus, many processes were required to perform with GPU hardware for complex computational processes. Hence, the proposed optimal multilayer CNN architecture contained 2D spatial information in the fractional-order convolutional layer (with two fractional-order convolutional windows), and continuously enhanced the features with two-round convolutional-pooling processes (with 16 Kernel convolutional windows and 16 maximum pooling windows), which could extract the desired features at different scales and different levels. Thus, in comparison with the other deep-learning methods, the proposed multilayer classifier exhibited promising results for the desired medical diagnostic purpose.