# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 1 Introduction
### Kidney Cancer  
The kidneys in the abdominal region are essential for bodily functions, containing connective tissue and fat. They dispose of metabolized compounds, maintain water and salt balance, and produce hormones for red blood cell production and blood pressure regulation. Figure 1 presents a visual depiction of the human kidneys. However, risk factors like smoking, obesity, and hypertension can compromise kidney function, leading to kidney cancer. DNA alterations can induce kidney cells to deviate from their fundamental role. Early detection is crucial for successful treatment and increased survival chances. Medical imaging tools like CT reveal the complex abdominal interior structure, often used for early-stage cancer detection [43; 44]. Renal cancer ranks tenth globally, with a significant increase in individuals. In the US, 2023 projections predict 81,800 new RC diagnoses, resulting in 14,890 deaths [45]. Renal cancer is a diverse group of tumors originating from different kidney cell types, with renal cell carcinoma (RCC) being the most aggressive form, accounting for 70%. of all RC cases [46]. The prevalent subtypes of renal cell carcinomas (RCCs) encompass clear cell, chromophore, oncocytoma, papillary, and other RCC subcategories. Accurate classification of histological subtypes within renal cell carcinoma is paramount to prevent unnecessary biopsies or surgeries [47]. By the World Health Organization (WHO) [48], The classification of RCC subtypes is of great importance as each type has its prognosis [48]. Conventional diagnostic methods may lead to misclassification of benign lesions like angiomyolipoma (AML) and oncocytoma (ONC) as RCC [49]. Incorrectly identifying benign lesions can lead to unwarranted surgical interventions, with 15-20. of tumors surgically removed with a preoperative RCC diagnosis potentially being angiomyolipoma (AML) [50]. Early and accurate diagnosis of renal tumors is crucial for treatment. Conventional tests like CBC, urinalysis, and blood assays can hint at RC but do not provide precise diagnoses or differentiate between subtypes, grades, or stages. Therefore, biopsy remains the ultimate benchmark for definitive RC diagnosis, as it accurately quantifies red blood cells, identifies blood, bacteria, or malignant cells, and gauges renal function [45]. Deep learning is a powerful machine learning technology that can autonomously acquire various features and patterns without human intervention [51; 52]. DL has revolutionized prognostic models for early tumor detection using pattern analysis techniques, demonstrating superior performance over conventional machine learning due to its exceptional precision in delivering results [53]. Object detection, a technique used in image processing, has gained significant interest in medical radiology due to its focus on extracting valuable insights from images. This concept includes single-class and multiclass object detection, crucial in identifying object categories [54]. DL methodologies have significantly improved performance and automated segmentation models, especially with the latest advancements in convolutional neural networks (CNNs) [52; 55]. CNNs are ideal for medical imaging because they can discern nuanced details. The SE-ResNet architecture enhances feature representation, highlighting relevant image structures and enabling CNNs to outperform traditional methods for more precise and efficient segmentation. In this study, the investigation delves into the utilization of five architectures from the SE-ResNet lineage. Additionally, the study involves a comparison of outcomes across different SE-ResNet backbones.