Kidney Tumor Detection Using Computed Tomography Scan Images Through CNN

The research in the current decade addresses a critical concern in the field of medical imaging, specifically focusing on the identification of kidney tumors within Computed Tomography (CT) scan images. The complexity of this task is highlighted by subtle variations in tumor appearance and the intricate nature of the dataset. To tackle these challenges, a robust solution employing Convolutional Neural Networks (CNN) is proposed. The study emphasizes the immense potential of DL in the realm of medical image classification, providing a powerful tool for enhancing diagnostic accuracy in the detection of kidney tumors. The CNN achieves a test accuracy of 94.99% and the pretrained ResNet50 model showcases even higher accuracy of 96.56%. The integration of these models into healthcare systems holds promise for enhancing diagnostic accuracy and reliability. The demonstrated success of the proposed approach emphasizes its practical applicability in clinical settings. The efficacy of kidney tumor detection methodology is offered in real-world healthcare scenarios.