## 5 Discussion  
As shown by the experiments, our proposed model provides a better evaluation performance in accuracy, sensitivity, specificity, and the F-measure compared to several previous studies on the four different datasets. Our proposed model includes algorithms such as WT, thresholding, morphological operations, ML, M-FD + GA, and ANN that provide several features. WT is effective in dividing images into different sub-bands and separates high frequencies from low frequencies, which can be helpful in noise capturing and reducing. The thresholding technique has been used due to the remarkable intensity variation between foreground tissues and the background of mammograms. Morphological operations have been used to remove small objects that remain in the binary images. Due to the homogeneity between the pectoral muscle and ROI, an ML technique was built based on the histogram of the oriented gradient feature with neural network classifiers to determine the region of the pectoral muscle and ROI. Based on our evaluation and comparison between FD and LBP, this study has motivated us to improve FD and propose M-FD for feature extraction, selecting the most relevant feature from the extracted features that GA has employed. Class discrimination information is maintained by the selected features that help in proper breast cancer classification on mammogram images [67]. As a result, the proposed technique is an efficient technique to discover subtypes of breast cancer in mammogram images, which has been supported by the evaluation of the proposed technique on four different datasets.  
However, the proposed method presents some limitations. Preprocessing mainly aims to enhance the texture and obtain powerful texture features from the image. The application of the proposed enhancement model removed some important information that may help identify the risk of malignancy. Thus, threshold selection is considered an important step in denoising-based image enhancement. Selection should be performed accurately and carefully because noise will still exist in the image when a small threshold is selected, whereas details of the image will be destroyed whilst producing artifacts and blurs when a large threshold is selected. Hence, a suitable threshold value must be determined to address this limitation and avoid denoising-based overfitting and underfitting in the proposed model. Moreover, low data contrast affects the feature extraction stage because there is some overlap between texture features of benign and malignant samples in this  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Performance (\%)**} \\ \cline{3-5}  & & **Ac** & **Sn** & **Sp** \\ \hline \multirow{3}{*}{Mini-MIAS} & DenseNet & 90.21 & 89.84 & 90.62 \\ \cline{2-5}  & DenseNet-II & 91.3 & 90.42 & 91.83 \\ \cline{2-5}  & Proposed M-FD model & 100 & 100 & 100 \\ \hline \multirow{3}{*}{DDSM} & DenseNet & 95.08 & 94.6 & 96.1 \\ \cline{2-5}  & DenseNet-II & 96.1 & 95.91 & 96.57 \\ \cline{2-5}  & Proposed M-FD model & 99.89 & 99.79 & 100 \\ \hline \multirow{3}{*}{INbreast} & DenseNet & 87.77 & 86 & 88.3 \\ \cline{2-5}  & DenseNet-II & 89.22 & 89.07 & 88.28 \\ \cline{2-5}  & Proposed M-FD model & 99 & 98.44 & 100 \\ \hline \multirow{3}{*}{BCDR} & DenseNet & 98.3 & 97.8 & 98.72 \\ \cline{2-5}  & DenseNet-II & 98.9 & 99.8 & 98.5 \\ \cline{1-1} \cline{2-5}  & Proposed M-FD model & 99.59 & 99.53 & 99.67 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of the proposed method with DenseNet and DenseNet-II models.  
stage. The use of texture features alone is insufficient to identify abnormalities and describe cases. Therefore, combining texture features with other features, such as geometry features, is reasonable.