AI-Powered Segmentation for Kidney Tumor Detection

Abstract

In medical imaging, kidney tumour detection is a crucial task that necessitates accurate segmentation for efficient diagnosis and therapy planning. In order to improve tumour segmentation accuracy, we provide a unique deep learning-based approach in this work that combines TensorFlow, Lent Convolutional Neural Network (CNN), and U-Net. To increase the visibility of features important for tumour diagnosis, our method starts with picture preparation techniques. For first feature extraction, the Lent CNN is used to extract key representations from medical images. Then, for accurate tumour border delineation, the U-Net architecture—which is renowned for its exceptional performance in semantic segmentation—is employed. TensorFlow is used for the implementation, guaranteeing scalability and computational efficiency. We test the segmentation accuracy and resilience of our suggested approach using benchmark kidney tumour datasets in order to determine its efficiency. Our model exhibits improved accuracy, precision, and recall when compared to existing methods, indicating its potential for early tumour diagnosis. Better clinical decision-making is made possible by this research, which advances AI-driven medical picture processing, especially in oncology. Our ultimate goal is to improve patient outcomes and prognosis in the treatment of kidney cancer.