EfficientNet family U-Net models for deep learning semantic segmentation of kidney tumors on CT images

Abstract

IntroductionKidney tumors are common cancer in advanced age, and providing early detection is crucial. Medical imaging and deep learning methods are increasingly attractive for identifying and segmenting kidney tumors. Convolutional neural networks have successfully classified and segmented images, enabling clinicians to recognize and segment tumors effectively. CT scans of kidneys aid in tumor assessment and morphology study, using semantic segmentation techniques for pixel-level identification of kidney and surrounding anatomy. Accurate diagnostic procedures are crucial for early detection of kidney cancer.MethodsThis paper proposes an EfficientNet model for complex segmentation by linking the encoder stage EfficientNet with U-Net. This model represents a more successful system with improved encoder and decoder features. The Intersection over Union (IoU) metric quantifies model performance.Results and DiscussionThe EfficientNet models showed high IoU_Scores for background, kidney, and tumor segmentation, with mean IoU_Scores ranging from 0.976 for B0 to 0.980 for B4. B7 received the highest IoU_Score for segmenting kidneys, while B4 received the highest for segmenting tumors. The study utilizes the KiTS19 dataset for contrast-enhanced CT images. Using Semantic segmentation for EfficientNet Family U-Net Models, our method proved even more reliable and will aid doctors in accurate tumor detection and image classification for early diagnosis.