# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 3 Results and Discussion
### Discussion  
#### 3.2.1 Result Analysis  
ImageNet assessed and optimized best-performing models, despite patch-wise classification using local features [48]. This research investigates the architecture's ability to extract global features from CT images of renal cancer and classify unseen images, revealing that extracting nuclei and tissue organization features is more valuable than nuclei-scale features for deciphering image classes. Evaluated different models based on their performance and employed a metric called Mean IoU (Intersection over Union) to assess the accuracy of the models. The study compared several models based on the SE-ResNet architecture with FPN (Feature Pyramid Network) and presented the results in Table 4. Each model was evaluated regarding background IoU score, kidney IoU score, tumor IoU score, and mean IoU score. The IoU scores indicate how well the models performed in segmenting different regions of the CT images. A higher IoU score signifies better performance, indicating higher overlap between the predicted and ground truth regions. The results demonstrate that all the SE-ResNet models achieved excellent IoU scores across the different categories. For example, the 'Seresnet18' model achieved a background IoU score of 0.999, a kidney IoU score of 0.972, a tumor IoU score of 0.971, and a mean IoU score of 0.981. Similarly, the other models, 'Seresnet34', 'Seresnet50','Seresnet101' and 'Seresnet152,' also achieved high IoU scores across the categories. Based on these results, it can be inferred that the SE-ResNet models \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Settings** \\ \hline Activation & ‘softmax’ \\ Optimizer & Adam \\ Loss function & Focal Loss, Dice Loss \\ Learning rate & 0.0001 \\ Batch size & 32 \\ Epochs & 50 \\ Metrics & IoU, F1-score \\ Input images size & \(128\times 128\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Models’ hyperparameter setup. \begin{table}
\begin{tabular}{c c} \hline \hline
**IoU Scores** & **Obtained Values** \\ \hline Background IoU Score & 0.999 \\ Kidney IoU Score & 0.980 \\ Tumor IoU Score & 0.984 \\ Mean IoU Score & 0.988 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Outcomes of the Ensemble Model of Seresnet50. with FPN architecture effectively extracted global features from CT images of renal cancer and utilized them to classify unseen images accurately. Figure 4 compares results for the SE-ResNet with FPN Architectures Using Mean IoU. Table 5 presents the results of the SE-ResNet models in terms of accuracy and loss. IoU score (Intersection over Union) and F1-score using validation images for evaluation. These metrics provide insights into the performance of the models in terms of overall accuracy and their ability to accurately segment and classify objects in the images. The accuracy represents the percentage of correctly classified instances from the total instances in the validation dataset. In this case, the accuracy is measured by the loss value, which is inversely proportional to accuracy. Therefore, a lower loss value indicates higher accuracy. For all the SE-ResNet models, including 'Seresnet18', 'Seresnet34', 'Seresnet50', 'Seresnet101', and 'Seresnet152' the loss values are around 0.752, indicating good accuracy in the range of 75.2%. The IoU score, the Jaccard Index, measures the overlap between the predicted and ground truth segmentation. A higher IoU score indicates better performance in accurately identifying and localizing objects. The SE-ResNet models achieved high IoU scores, ranging from 0.979 to 0.987. For example, the 'Seresnet18' and 'Seresnet34' models achieved an IoU score of 0.979, while 'Seresnet50' achieved a slightly higher score of 0.987. Similarly, 'Seresnet101' and 'Seresnet152' achieved IoU scores of 0.985 and 0.986, respectively. These scores suggest that the models effectively segment the objects of interest in the validation images.