| dc.description.abstract | One of the most common types of kidney cancer is renal cell carcinoma. Renal cell
carcinoma is divided into three types, Clear Cell Carcinoma, Papillary Renal Cell
Carcinoma, and Chromophobe Renal Cell Carcinoma. This cancer classification can
be done by radiological examination through CT scan medical imaging. However, to
classify the cancer, expert doctors diagnose it manually first. Therefore we need a
method that can perform early processing of radiological detection through medical
images quickly and accurately. This research uses Probabilistic Neural Network
(PNN). The data used were CT scan of 3 types of Renal Cell Carcinoma as much as 211
data which is then divided into two parts, 151 data as training data and 60 test data.
The first stage is to divide the data into training data and test data. Then, Grayscaling,
Scaling and Contrast Limited Adaptive Histogram Equalization (CLAHE) were
performed at the preprocessing stage. Then, the segmentation stage uses the
Thresholding method. Then, the Feature Extraction process is carried out using the
Gray level Co-occurance Matrix (GLCM) method. The last stage is classification using
PNN, the results of the classification are Clear Cell Carcinoma, Papillary Renal Cell
Carcinoma, and Chromophobe Renal Cell Carcinoma. The test results reach an
accuracy of 90%. | en_US |
