## 3 Materials and Method
### Methodology  
#### 3.2.1 Motivation  
For Brest cancer detection, DL algorithms may be an effective method. Similar issues have been addressed using DL techniques, including the classification of skin cancer [48], the classification of Parkinson's disease and other brain disorders, and the diagnosis of pneumonia using chest radiograph images. In [29] the authors proposed custom CNN \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**DATASETS** & **BENIGN** & **MALIGNANT** & **NORMAL** & **TOTAL** \\ \hline DATASET1 & 437 & 210 & 133 & 780 \\ DATASET2 & 100 & 150 & - & 250 \\ TOTAL & 537 & 360 & 133 & 1030 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detail of BC dataset. Figure 1: **BC ultrasound images from dataset. **  
model for BC ultrasound image detection. The proposed consist of only one convolution layer with 20 filters. Similarly, in [31] the authors presented CNN for BC detection (CNNI-BCC). This framework uses a supervised DL neural network to do the classification. The experimental findings demonstrated that CNNI-BCC performed better than previously conducted research and achieved an accuracy of 89.47%.Furthermore, in [32] the authors presented their own proposed CNN model for the diagnosis of BC in their study. In addition, they employed a TL technique to combine nine different pre-trained DL models to classify two BC datasets. The suggested model obtained the finest accuracy of 99.5%. Subsequently, motivated by the effectiveness of DL-based architecture in detecting breast cancer using certain images. In this study, we suggested a DeepbreastcancerNet model for detecting and classifying breast cancer. The objective of this study is to suggest a DL model that can more accurately and successfully detect breast cancer from ultrasound images. The depth and input image resolution of the proposed model are based on the following information: It is generally accepted that a deeper DL-based model improves the network's classification performance by capturing more intricate and important deep features [49; 50]. In addition, depth scaling was widely used by various DL-based models to increase their accuracy. However, when the network's depth increases, computational complexity rises as well, therefore accuracy may not always be improved. In order to achieve better performance, DL-based models gather more detailed features from high-resolution input photos. DL models can detect images with resolutions ranging from \(224\times 224\) to M and \(299\times 299\), however, models with greater resolution often perform better. Similar to that, the suggested model has 24 layers and is capable of processing images with a 224 by 224 pixel resolution. The DeepbreastCancerNet architecture and input image size are chosen based on the needs of the current computational infrastructure. In the suggested architecture, we utilized both cross-channel and batch normalization (BN) layers. Cross-channel normalization layer because it enhances generalization and lowers top-1 and top-5 error rates. Batch normalization reduces internal covariate shifting, which speeds up model training to address this issue, we used a leaky ReLu in the DeepbreastcancerNet strategy that was recommended [51]. To resolve this problem, we employed a leaky ReLu in the DeepbreastcancerNet strategy that was suggested. When the unit is inactive, the Leaky ReLu activation method permits a small (non-zero) gradient. As a result, it keeps learning instead of coming to a stop or hitting a brick wall. Additionally, Max pooling is used in our suggested study since it keeps the feature maps' most salient features, producing statistical basis features. Additionally, Max pooling is used in our proposed study as it retains the most significant features of the feature maps, producing precise classification features. For down sampling, max-pooling is employed with a filter size of 3\(\times\)3, At the conclusion of the structure, a global average pooling is utilized to combine each feature map into a single value.