Kid-ML: ML For Kidney Malignant Tissues Identification

Abstract

A considerable worldwide medical and health burden is imposed by kidney disease due to its high rates of morbidity and death as well as its high economic cost. Imaging tests can be used by doctors to detect kidney tumors or other diseases. Imaging studies include Magnetic Resonance Imaging(MRI), Computed Tomography(CT) scan, and ultrasound scan which consume a lot of time from doctors to detect kidney cancers through them. In order to help doctors to identify tumors in their early stages, they can use simple Machine Learning(ML) techniques or Deep Learning techniques through diagnostics and predictions applications. A rise in interest in deep learning algorithms, which are Artificially Intelligently (AI) based, on a worldwide scale has enabled recent improvements in medical imaging and kidney segmentation. Deep Learning techniques which are AI-based can offer and identify the kidney tumor in a more efficient method, allowing for the development of a more effective kidney tumor detection system. An input layer, one or more hidden layers, and an output layer