## 7 Conclusions  
As in many medical decision support systems, deep learning methods have begun to play a crucial role in diagnosing and the early detection of breast cancer. Breast cancer is one of the most diagnosed and deadliest cancer types in women. The early diagnosis of breast cancer is important for its treatment. In this study, the VGG16, MobileNet, and DenseNet201 models and a custom-built CNN model were employed to classify breast cancer. To enhance the capability of the custom-built CNN model, the weight of the final dense layer was optimized using the PSO algorithm. The Breast Histopathology Images Data Set was used to calculate model performance. This dataset was used to form two distinct datasets: one with 157,572 images of size 50 \(\times\) 50 \(\times\) 3 (experimental study 1) and another with 1116 images of size 224 \(\times\) 224 \(\times\) 3 (experimental study 2). This analysis aimed to determine how variations in image dimensions, dataset size, and model architecture affect classification results. Results from experimental study 1, using a larger dataset of 50 \(\times\) 50 \(\times\) 3 images, revealed that all deep learning models achieved high accuracy. In the customized transfer learning models applied within deep learning methods, the original dimensions of the images in the dataset were used as input instead of the default image input sizes. The most efficient model in terms of performance and low computational cost was the custom-built CNN model, which had the fastest training time, the lowest FLOPS value (0.0889 G), and the highest accuracy rate (93.80%). The proposed model, with its high performance and low computational cost, was particularly notable for its balanced metrics across accuracy, precision, recall, and F1 score. In experimental study 2, where a smaller balanced dataset of 1116 images at 224 \(\times\) 224 \(\times\) 3 dimensions was used, all models also achieved high accuracy. Among these models, MobileNet performed exceptionally well, achieving the highest accuracy of 95.54%. The custom-built CNN model achieved an accuracy of 92.86%, demonstrating a favorable balance between high performance, low computational cost, and shorter training time. These results also demonstrate that the proposed methods are not prone to overfitting and support these findings. In addition, the study revealed that while models trained with the larger dataset (224 \(\times\) 224 \(\times\) 3) achieved higher precision and overall performance, they suffered from lower recall and F1 scores compared to models trained with the smaller dataset (50 \(\times\) 50 \(\times\) 3), highlighting the significant impact of dataset size on model performance. In future studies, many mammography images of the same patients can be included together with breast cancer histopathology images collected from new patients. The datasets created from both types of images can be used cooperatively to diagnose cancer and its variants using deep learning methods, which depend on the diversity of the data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Method** & **Dataset** & **Accuracy Rates** \\ \hline
[35] & MobileNetV2 and EfficientNet & \
\begin{tabular}{c} (Breast Histopathology \\ Images, Kaggle) \\ \end{tabular} &
\begin{tabular}{c} MobileNetV2: 92.35\% \\ EfficientNet: 91.02\% \\ \end{tabular} \\ \hline
**This Study** & \
\begin{tabular}{c} (Breast Histopathology \\ VGG16 \\ \end{tabular} &
\begin{tabular}{c} MobileNetV2: 92.35\% \\ EfficientNet: 91.02\% \\ \end{tabular} \\ \hline
**Table 3**: _Cont._ & Experimental Study 1 \\ \hline
**Table 3**: _Cont._ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._In addition, the models' high accuracy in this study indicates their potential for clinical use; nevertheless, more research is necessary to assess how well they integrate into real-world diagnostic procedures.