## 5 Discussion
Similarly, Figure 14 contains the images classified as false positive (FP) and the corresponding Grad-CAM heatmap histograms. The original images shown in both figures are in color (RGB) format, representing histopathology images. The histograms in these figures represent the distribution of activation values (between zero and one) in the Grad-CAM heat maps, not the pixel intensity distribution of the original RGB images. The Grad-CAM heatmaps visualize the image regions that the model finds important during classification, making it possible to understand the model's decision mechanism and analyze the factors that cause classification errors. These visualizations play a critical role in evaluating the model's behavior and identifying  
Figure 14: Sample images classified as false positives, and heatmap histogram graphs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Number of Data: 157,572** \\ **Image Size: 50 \(\times\) 50 \(\times\) 3** \\ \end{tabular} & **Accuracy** & \begin{tabular}{c} **FLOPS** \\ **(Batch\_Size = 1)** \\ \end{tabular} & \begin{tabular}{c} **Training Time** \\ **(Second)** \\ \end{tabular} \\ \hline VGG16 & \begin{tabular}{c} Batch\_size: 200 \\ Epoch:50 \\ \end{tabular} & 92.99\% & 1.5 G & 1660.61 \\ MobileNet & \begin{tabular}{c} Batch\_size: 200 \\ Epoch:50 \\ \end{tabular} & 92.75\% & 0.049 G & 1025.61 \\ DenseNet201 & \
\begin{tabular}{c} Batch\_size: 200 \\ Epoch:50 \\ \end{tabular} & 90.18\% & 0.399 G & 4858.08 \\ The custom-built &
\begin{tabular}{c} Batch\_size: 200 \\ CNN model \\ \end{tabular} & 93.80\% & 0.0889 G & 789.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy rates, computational complexities, and training times of deep learning models obtained as a result of experimental study 1. As shown in Table 1, the custom-built CNN yielded the most efficient model in terms of both best performance and low computational cost by providing the highest accuracy rate of 93.80%, the lowest floating-point operations per second (FLOPS) value with 0.0889 G FLOPS, and the shortest training time. On the other hand, the VGG16 and MobileNet models gave similar results in terms of accuracy rates, and while the VGG16 model required more computation with a higher FLOPS value, the MobileNet model offered similar performance with a lower FLOPS. The short training time and high accuracy of the custom-built CNN model make it the most effective model. In terms of performance and cost, the CNN model has the best performance to cost ratio with high accuracy and low computational costs. The MobileNet model is an efficient alternative with a fast-training time and low FLOPS; however, its accuracy is lower than that of the CNN model. In conclusion, the custom-built CNN model demonstrated superiority over transfer learning models in terms of performance and cost-effectiveness. Table 2 presents the results of the deep learning models obtained from experimental study 2. As shown in Table 2, the MobileNet model demonstrated the highest performance with an accuracy rate of 95.54%. This model has low computational costs with a FLOPS value of 1.15 G and a training time of about 4 min, making it the most efficient model. The custom-built CNN model, which followed the MobileNet model, achieved an accuracy of 92.86%, a FLOPS value of 2.34 G, and a training time of approximately 2 min. The proposed model offers a combination of high accuracy, low computational cost, and short training time, making it an ideal option for large datasets and scenarios with limited computational resources. The DenseNet201 model provides an intermediate balance with 92.86% accuracy, 8.63 G FLOPS, and approximately 10 min of training time, whereas the VGG16 model, with 30.8 G FLOPS and approximately 3 min of training time, requires high computational cost and delivers the lowest performance with 86.60% accuracy. In addition, in Figure 15, a comparison of experimental study 1 and experimental study 2 results is presented.