Kidney Tumor Detection Using MLflow, DVC and Deep Learning

Kidney tumors are a global health concern, necessitating precise detection for effective treatment and better patient outcomes. This paper presents a novel approach that combines deep learning with Data Version Control (DVC) and MLflow frameworks to revolutionize kidney cancer detection. Deep learning, a subset of artificial intelligence, shows great promise in interpreting complex patterns in medical imaging data. Utilizing convolutional neural networks (CNNs), our model analyzes radiographic images to identify subtle indicators of renal malignancies with unprecedented accuracy and efficiency. Incorporating DVC ensures seamless management of large imaging datasets, promoting collaboration and reproducibility across research endeavors. Additionally, MLflow streamlines the experimentation process, enabling systematic evaluation of model performance metrics and hyperparameters. Through meticulous logging and visualization of experimentation results, our framework facilitates informed decision-making, leading to the selection of optimal models for kidney cancer detection. This comprehensive approach signifies a significant advancement in oncological diagnostics, offering a holistic solution to the challenges posed by kidney cancer. By merging deep learning with DVC and MLflow, our methodology heralds a transformative paradigm shift in cancer detection, poised to enhance clinical outcomes and elevate patient care globally.