Advancing Kidney Tumor Detection in CT Scans with a Hybrid Computational Framework

Abstract

Kidney diseases such as cysts, tumors, and stones are life-threatening conditions that require early and accurate detection for effective treatment. Traditional diagnostic methods using CT scans often depend on manual interpretation by radiologists, which can be time-consuming and prone to error. To address this challenge, we propose a deep learning–based automated system for multi-class kidney disease classification using CT images. The system utilizes EfficientNetV2B0, a state-of-the-art convolutional neural network, to extract deep features from CT scans. A custom classification head with Global Average Pooling, Dropout, and Dense layers is employed to classify images into four categories: Normal, Cyst, Tumor, and Stone. Data augmentation and class weighting are applied to handle dataset imbalance and improve generalization. The model achieves high accuracy and robustness, outperforming conventional CNN approaches. Furthermore, the trained model is integrated into a Flask web application, providing a user-friendly interface with functionality for image upload, real-time prediction with confidence scores, and visualization of training results through charts. This approach demonstrates the potential of advanced deep learning models combined with web deployment to support radiologists in fast, reliable, and scalable kidney disease diagnosis.