Bladder cancer on a Computed Tomography Scanner (CT-Scan) image has a different shape, location and texture for each image. Each person's bladder is different in size when the image is taken. Contrast and non-contrast image captured on a CT scan of the bladder can be used to determine the structure and shape of the bladder. However, the difference in contrast images between an abnormality and a healthy bladder is often not visually obvious, making the evaluation is difficult. Although there have been many studies on bladder cancer detection based on CT images that have been carried out, it has been reported that the success rate for detecting bladder cancer is still relatively low. In this study, Computer-Aided Diagnosis (CAD) is used to help evaluating bladder abnormalities using the segmentation method based on an active contour algorithm. The Gray Level Co-Occurrence Matrix (GLCM)-based features of the images are used as the inputs of the Artificial Neural Network (ANN) to classify the normal and abnormal images. The research CAD in this study using MATLAB. A total number of samples were 320 images with 200 abnormal (25 patient), and 120 normal (8 patient) images were used as training and testing data. The result based on Receiver Operating Characteristic (ROC) illustrated that the training accuracy was 90,2 ± 2.68 %, and the test accuracy was 89,2 ± 2,95%. These results mean that this developed CAD system can recognize normal and abnormal bladder images.