Abstract
Calcification is a commonly observed disease and is of peo- ple prepared by staurologists using computed (CT) imaging, and the number of computerized diseases has increased ex- ponentially in industrialized countries. This abnormality is when the urine has more calcium than normal substances such as and oxalate. The changing habits of today’s society form an early detection that late in the kidneys, an advanced detection for an early stage is extremely crucial, an advanced detection for an advanced stage is crucial to help dissolve and eliminate the stone. This work presents an evaluation of the pre-processing methods: Median Filter and CLAHE, with pre-trained descriptors DenseNet201 , VGG16 , RESNET50 and Xception , and the Multi-layer Perceptron (MLP) and Random Forest (RF), for detection of calcifications on CT images of the urinary tract. For the evaluation of the pro- posed method, 10 exams were used, totaling 2,790 images with calcification and 2,312 images without calcifications. The evaluated scenario (With Calc x Without Calification) and the best results were obtained with the X descriptor of 0.94.86.
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