## 3 Experimental Results and Discussion
### Discussion
In contrast to the ML-based methods, the DL-based methods, such as TTCNN, Grad-CAM CNN, DNN (deep neural network), FCN (fully convolutional network), attention dense-Unet, and dense-Unet models [26; 27; 28; 29; 37; 38; 60], had more complex schemes to set up the classifiers with minimal expert interventions, which used the large volumes of unstructured datasets to train a classifier for classification or detection purposes. For example, TTCNN [37] comprised two convolutional layers (with 5 \(\times\) 5 kernel mask size, 16 and 32 kernel masks) and followed by the MP layers (with 2 \(\times\) 2 mask size, 16 and 32 MP masks), the 3 rd convolutional layer (with 3 \(\times\) 3 kernel mask size, 64 kernel masks), and the fully connected layer (classification layer). With the DDSM [19; 62], INbreast [63], and MIAS [22; 23] databases, the TTCNN had accuracy rates of 99.08%, 96.82%, and 96.57% for breast cancer diagnosis and classification, respectively. The Grad-CAM-based CNN, including DenseNet-169 and EfficientNet-B5 [38], could detect malignant lesions in both craniocandal and mediolateral oblique view images, which highlighted ROI with the red color-coded areas to indicate the positive region for identified suspicious lesions. This visualization manner could locate and identify the abnormalities from mammograms in case of mass or calcification. DenseNet-169 and EfficientNet-B5 had mean accuracy rates of 88.1% and 87.9% for automated breast cancer detection, respectively. Thus, these multiconvolutional-pooling layers were used to select the 2D features for improved image contrast (contrast adjustment), which limited the size of output patterns, and refined the classifier's recognition ability. However, the model's purpose and performance were required to continuously maintain the available training dataset, and the excessive multiconvolutional-pooling processes would decrease the position, orientation, and spatial relationships of the desired object. ML-based methods could be rapidly established but might be limited in their results for their applications; and DL-based methods required more time to set up the model but could rapidly produce results and had promising classification accuracy with the multiconvolutional processes. In addition, their models required the resource of GPU hardware to perform the multiconvolutional-pooling processes and the classifier's training tasks. Therefore, we integrated the 2D spatial and 1D CNN-based classifier to simplify the 2D multiconvolutional processes and computational complexity levels. In the classification layer, the GRA-based classifier had straightforward mathematic operations, without optimization/fine-tuning algorithms and iteration computations, to perform the training and pattern recognition tasks. Some advantages of the proposed classifier are shown below:  
* The possible breast lesions' spatial and edge information could be enhanced by the II-based spatial convolutional process in the first convolutional layer, which helped to easily locate ROI and extract feature patterns from the original mammographic image;
* The suitable two-round 1D convolutional processes could quantify the different levels, which helped to preliminary separate the Nor from the B and M classes;
* The dimension of feature signals could be reduced by the 1D pooling process, which helped to overcome the classifier's overfitting problems in the training stage;
* The straightforward mathematic operations performed the training and pattern recognition tasks;
* The optimal parameters that were updated in the training stage did not require convergence condition assignment and parameters adjustment;
* The determination network parameters did not require complex iteration computations and optimization algorithms. * The classification accuracy could be obtained in less computation time and was feasible to replace manual screening with specific expertise and experience.