TY - GEN
T1 - Computer-aided diagnosis (CAD) to detect bladder abnormality based on CT images using artificial neural network (ANN)
AU - Hariyani, D. A.
AU - Prajitno, P.
AU - Soejoko, D. S.
N1 - Funding Information:
The authors gratefully acknowledge grants by University Indonesia (Hibah PUTI Prosiding 2020, Grant Number: NKB-996/UN2.RST/HKP.05.00/2020) for this research and publication. In addition, the authors would like to thank my supervisor who has provided direction, guidance and support for the completion of this study.
Publisher Copyright:
© 2021 Author(s).
PY - 2021/3/29
Y1 - 2021/3/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85103710451&partnerID=8YFLogxK
U2 - 10.1063/5.0047830
DO - 10.1063/5.0047830
M3 - Conference contribution
AN - SCOPUS:85103710451
T3 - AIP Conference Proceedings
BT - Proceedings of the International Conference and School on Physics in Medicine and Biosystem, ICSPMB 2020
A2 - Lubis, Lukmanda Evan
A2 - Nuzulia, Nur Aisyah
A2 - Hidayati, Nur Rahmah
PB - American Institute of Physics Inc.
T2 - 2020 International Conference and School on Physics in Medicine and Biosystem: Physics Contribution in Medicine and Biomedical Applications, ICSPMB 2020
Y2 - 6 November 2020 through 8 November 2020
ER -
