Computer-aided diagnosis (CAD) to detect bladder abnormality based on CT images using artificial neural network (ANN)

D. A. Hariyani, P. Prajitno, D. S. Soejoko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International Conference and School on Physics in Medicine and Biosystem, ICSPMB 2020
Subtitle of host publicationPhysics Contribution in Medicine and Biomedical Applications
EditorsLukmanda Evan Lubis, Nur Aisyah Nuzulia, Nur Rahmah Hidayati
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440876
DOIs
Publication statusPublished - 29 Mar 2021
Event2020 International Conference and School on Physics in Medicine and Biosystem: Physics Contribution in Medicine and Biomedical Applications, ICSPMB 2020 - Depok, Virtual, Indonesia
Duration: 6 Nov 20208 Nov 2020

Publication series

NameAIP Conference Proceedings
Volume2346
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2020 International Conference and School on Physics in Medicine and Biosystem: Physics Contribution in Medicine and Biomedical Applications, ICSPMB 2020
Country/TerritoryIndonesia
CityDepok, Virtual
Period6/11/208/11/20

Fingerprint

Dive into the research topics of 'Computer-aided diagnosis (CAD) to detect bladder abnormality based on CT images using artificial neural network (ANN)'. Together they form a unique fingerprint.

Cite this