Affiliations: [a] QEC, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan | [b] Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan | [c] Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA | [d] Department of CS and IT, University of Poonch Rawalakot, Rawalakot, Azad Kashmir, Pakistan
Correspondence:
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Corresponding author: Lal Hussain, QEC, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan. E-mail: Lall_hussain2008@live.com.
Abstract: Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.
