Diagnosis of Kidney Renal Cell Tumor through Clinical data mining and CT scan image processing: A Survey
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Kidney Renal Cell Carcinoma, Contrast enhancement, data mining, medical image processing, Kidney tumor, pathology
Abstract
This study deals with the systematic study of the mining of data and medical image-based CAD to classify or predict Kidney Renal (KRCC) tumors. Kidney tumors are of different types having different characteristics and have different methodologies to classify or predict tumor and its stages. KRCC is the most common type of cancer of the kidney, but there are others. Several factors may increase the risk of a person developing KRCC disease like smoking, obesity, High blood pressure, and many more. In almost all cases, only a single kidney is affected, but in rare cases, both can be affected by KRCC. As cancer grows, it may invade structures near the kidney, such as surrounding fatty tissue, veins, renal gland, or the liver. It might also spread to other parts of the body, such as the lungs or bones. It becomes essential to detect the KRCC tumor and classify it at the early stage to assist the pathologist in identifying the cause and severity of the tumor and in monitoring treatment. The pathologist examines the kidney diseases by using two different modes of data (Medical images and clinical databases). In this study, we reviewed different CAD tools to classify or predict KRCC tumor and its stages. For this study, two groups of methods that are data mining and medical image processing methods are selected. These methods allow the accurate quantification and classification of KRCC tumors from the clinical tools. Computer-assisted medical image and clinical database analysis show excellent potential for tumor diagnosis and monitoring.
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