Prostate Cancer Classification Technique on Pelvis CT Images - Sahrin, Uzair; Norul Huda Sheikh Abdullah, Siti; Omar, Khairuddin; Adam, Afzan; Sharis, Syazarina (International Journal of Engineering and Technology, 2019)
Prostate cancer is one of the commonest cancer found: the forth in Malaysia and sixth in the planet with 307 000 mortalities in 2012. Early detection is important to reduce the death rate; thus this research is carried out to develop an automated prostate cancer classification from the bone scan of pelvis CT images. Preliminary experiment has been carried out using real images from Hospital Chancellor Tuanku Muhriz (formerly known as UKM Medical Center) database. The detection algorithm was developed and compared between Random Forest and Logistic Regression classification of normal and abnormal cancerous prostate image. Furthermore, random tree was selected as the mining technique to model the knowledge in radiology department in predicting the level of prostate cancer severity. A total of 51 cases of prostate cancer patients with 2 urologists, a radiologist and an expert on pathology of PPUKM actively involved in the development of the expert system. The classification has achieved 90% accuracy percentage with 10-fold cross validation technique with Logistic Regression. In addition, the expert system grading on test set has achieved 96% accuracy with the same learning technique.