Advanced Automated Kidney Tumor Diagnosis Using a VGG-Based Deep Learning Model

Early detection of kidney cancers using medical imaging analysis has the potential to greatly improve patient outcomes. This study introduces a robust system that uses deep learning techniques to accurately and efficiently predict kidney cancers in their early stages. We used the KiTS19 dataset, which deals with renal tumor segmentation from CT scans, and worked with radiologists to assure its credibility and accuracy, which are critical for medical applications. Our base model was created by combining the VGG16 architecture with customized fully connected layers and applying transfer learning to pre-trained convolutional layers. This architecture made it easier to extract and classify features, which is important for tumor prediction. With the VGG16 model, we attained a prediction accuracy of 97.5%. Furthermore, we conducted a thorough comparison of VGG16 to other well-known deep learning models such as ResNet, Inception, MobileNet, and DenseNet. We also created a user-friendly interface to aid in the prediction of kidney tumors from CT scan pictures, making the model more accessible and simple to use. This study reveals a strong possibility for improving diagnostic accuracy and, ultimately, patient care outcomes in medical imaging analysis.