Exploring Innovations in Kidney Tumor Diagnosis: An Overview of Deep Learning and Machine Learning Techniques

Kidney tumor diagnosis is a very important job in medical imaging where accurate and efficient detection techniques are essential. Typically, robustizes are depend on handcrafted features, which result in ineptness for complex cases. This work investigates use of the deep learning (DL) and machine learning (ML) in combination with Internet of Things (IoT) in real time to detect and classify the tumor. Therefore, we develop a hybrid model that makes use of Convolutional Neural Networks (CNNs) for feature extraction, U-Net networks to perform segmentation and high-performance classification techniques such as InceptionV3 and Transformer based architectures. To solve overfitting problem and also to make model more precise technique of data augmentation, dropout and adaptive learning are used. Then, I evaluate the proposed model on a set of CT scans and show that it achieves greater accuracy than standard methods. On top of that, IoT integration improves diagnostic efficiency by providing real time data through real time processing of data. Experimental evidence of improved performance is shown with accuracy rates better than what is currently available. Future improvements as well as edge computing for time sensitive applications are further discussed in the study, and metrics for evaluation are introduced. In this research extensive efforts have been put in developing scalable and efficient AI based solution to kidney tumor diagnosis which offers significant enhancement in clinical decision making and patient outcomes.