A Deep Learning Approach Based on CT Images for an Automatic Detection of Polycystic Kidney Disease
S. Aruna, N. Deepa, T Devi.
Abstract
Deep Learning is a subdivision of machine learning; deep learning is mainly separated into multiple neural network layers. These layers allow it to learn large amounts of the data and try to mimic the human brain. By adding more hidden layers it can help to improvise the efficiency. Deep Learning techniques are utilized to identify diabetes mellitus, vascular dementia early diagnosis and mammography nodule ultrasonographic recognition. Pathology and Radiology will be invested in the future for new advancement in deep learning. Kidney irregularities is one of the most common diseases among people of all ages. A global shortage of urologists developed a deep learning model to diagnose kidney problems with this study. The kidney area is surrounded by a thick wall. It is possible to have multiple cysts with high moisture concentrations that cause renal diseases. The units from zero -twenty indicate different types of renal diseases. Tissues that can be grouped together form a solid lamp known as tumor. Tumors can cause direct damage to glands, spinal cells and other organs. Urolithiasis is another name for renal stone disease where a large amount of solid materials enter into the digestive tract. In a research paper, CNN is trained to learn how the cysts diagnosis should be done. Tree Convolution neural deep learning techniques VGG19 are used to detect kidney abnormalities. To train and evaluate a dataset of 4000 abdomen computed tomography with classifier and segmentation is created. The increased categorization and accuracy rate obtained to display the efficacy of kidney disorders is 98%.