FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images

Abstract

Kidney tumors are a significant health concern. Early detection and accurate segmentation of kidney tumors are crucial for timely and effective treatment, which can improve patient outcomes. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown great promise in medical image analysis, including identifying and segmenting kidney tumors. Computed tomography (CT) scans of kidneys aid in tumor assessment and morphology studies, employing semantic segmentation techniques for precise pixel-level identification of kidneys and surrounding anatomical structures. This paper proposes a Squeeze-and-Excitation-ResNet (SE-ResNet) model for segmentation by combining the encoder stage of SE-ResNet with the Feature Pyramid Network (FPN). The performance of the proposed SE-ResNet model is evaluated using the Intersection over Union (IoU) and F1-score metrics. Experimental results demonstrate that the SE-ResNet models achieve impressive IoU scores for background, kidney, and tumor segmentation, with mean IoU scores ranging from 0.988 to 0.981 for Seresnet50 and Seresnet18, respectively. Notably, Seresnet50 exhibits the highest IoU score for kidney segmentation. These findings suggest that SE-ResNet models accurately identify and segment regions of interest in CT images of renal carcinoma, with higher model versions generally exhibiting superior performance. The proposed Seresnet50 model is a good tool for accurate tumor detection and image classification, aiding medical professionals in early diagnosis and timely intervention.