# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 4 Conclusions  
In conclusion, using a SE-ResNet FPN model, this paper proposes a method for segmenting tumors and kidneys on CT slices. The proposed method comprises data preparation, segmentation, and an ensemble phase. Classifying CT images necessitates the preprocessing, downsampling, and resizing the images. In addition, the IoU and F1-score metrics are discussed as common evaluation metrics for image segmentation tasks. The paper concludes with a summary of related techniques in the literature. The proposed method can increase segmentation accuracy and kidney and tumor diagnosis on CT slices.  
The study investigated the performance of the Feature Pyramid Network (FPN) architecture in combination with different models from the SE-ResNet family for image segmentation, specifically in kidney cancer CT images. The findings indicated that the models fine-tuned on ImageNet surpassed patch-wise classification approaches regarding accuracy and sensitivity. The SE-ResNet models exhibited impressive results in segmenting the background, kidney, and tumor regions, as evidenced by mean IoU (Intersection over Union) scores ranging from 0.981 to 0.988. Seresnet50 demonstrated the highest IoU score of 0.988, indicating its effectiveness in accurately segmenting tumors. Comparison with prior studies further highlighted the superior performance of the models employed in this research, as they achieved higher IoU scores for kidney and tumor segmentation. These outcomes emphasize that combining the FPN architecture and SE-ResNet models can discern and segment the relevant regions of interest in kidney cancer CT images. Overall, this study underscores the potential of utilizing deep learning approaches, specifically the FPN architecture and SE-ResNet models, for precise and accurate image segmentation tasks in the context of kidney cancer histology analysis.  
Also, for the Dice similarity coefficient (DSC) Scores, Table 6 showcases the remarkable performance of the SE-ResNet model when dealing with the specific image segmentation tasks examined in this study. The models achieved impressive Dice Scores for background, kidney, and tumor segmentation, with mean Dice Scores ranging from 0.989 for Seresnet18 and Seresnet34. Notably, the performance generally improved as the SE-ResNet architecture version progressed from Seresnet18 to Seresnet152. For instance, the Dice Score increased from 0.989 for Seresnet18 to 0.993 for Seresnet50 and Seresnet152, then slightly declined for Seresnet101 to 0.992. The background segmentation Dice Scores were consistently exceptionally high, with a score of 0.999, across all SE-ResNet designs (Seresnet18-Seresnet152). Seresnet50 achieved the highest Dice Score of 0.990 for segmenting kidneys, while Seresnet50 and Seresnet152 obtained the top Dice Score of 0.992 for segmenting tumors. These findings demonstrate that the SE-ResNet designs effectively address the challenges posed by these segmentation tasks.  
The proposed strategy was evaluated on the KiTS19 benchmark dataset for kidney tumor segmentation. It would be intriguing to see how well the method performs on other medical imaging datasets, such as lung, brain, and liver imaging. Comparing the performance of the proposed method to that of other cutting-edge techniques on various datasets could help establish its generalizability and robustness. Exploration of other deep learning architectures: For image segmentation tasks, the authors of this paper used the FPN architecture in conjunction with SE-ResNet models. Nonetheless, many other deep learning architectures, including Attention U-Net, DeepLab, and Mask R-CNN, could be investigated for this purpose. Future research could compare the performance of various architectures to the performance of the proposed method.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.