A decision support system for detection of the renal cell cancer in the kidney. (July 2018)

• Record Type: Journal Article
• Authors: Tuncer, Seda Arslan; Alkan, Ahmet
• Is Part Of: Measurement. Volume 123(2018)
• Page Start: 298
• Page End: 303
• Publication Date: 2018-07
• Subjects: Spinal cord -- Renal cell cancer -- Decision support system -- K-Means; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.04.002
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The proposed decision support system detects renal cell cancer. Spinal cord was chosen reference for segmentation. Renal cell cancer detection involves two stages: segmentation and cancer detection. The proposed system is based on K-Means and SVM. Abstract: Renal cell cancer is the most common type of kidney cancer and usually occurs at an advanced ages. The rapid spread of renal cell cancer and the inability to detect the disease early often results in a fatality. Therefore, it is important to identify the renal abnormalities before the disease reaches the advanced phase. This paper proposes a decision support system that detects renal cell cancer using abdominal images of healthy and renal cell cancer tissues. Renal cell cancer detection involves two main stages as segmentation and cancer detection. In the first step, the kidney areas have been obtained by segmentation based on clustering analysis. In the second step, classification has been made by computer-assisted detection system to identify renal cell cancer. Feature vectors that support the originality of the study at this stage have been created. Subsequently, classification has been made using these feature vectors with the Support Vector Machines (SVMs). For detecting the renal abnormality, 130 different images obtained from the image archiving system of the Radiodiagnostic Department of Fırat University Medical Faculty were used. Thirty of these images have been used to train the K-means classifier. Performance evaluations have been made for both segmentation and classification. In order to measure segmentation success, the Dice coefficient was obtained as 89.3%. Sensitivity, Specificity, Accuracy, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) coefficients, which have been used to determine the classification performance, were obtained as 84%, 92%, 88%, 91.3% and 85.19% respectively.