Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier

A novel method to diagnose clinically significant prostate cancer (PCa) using Multi-parametric Magnetic Resonance Imaging (mpMRI) biomarkers in a highly imbalanced dataset is investigated in this paper. Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value (BVAL) Diffusion-Weighted (DW) images obtained from PROSTATEx 2016 challenge dataset publicly available in TCIA (The Cancer Imaging Archive) is used for this study. High-level features are extracted using a single layer Sparse Autoencoder (SAE). Synthetic Minority Oversampling Technique (SMOTE), Weka Resample algorithm and Adaptive Synthetic (ADASYN) sampling approach are explored to solve the class-imbalance problem. The performance of various classifiers are also investigated and it was found that the data augmented using ADASYN followed by classification using random forest classifier achieved the best performance. It achieved an area under ROC curve of 0.979. It also reached a Cohen's kappa score of 0.873, an accuracy of 93.65% and F-Measure of 0.94 in distinguishing clinically significant PCa from indolent Pca.

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Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).