MRI imaging texture features in prostate lesions classification | SpringerLink

(PCa) is the most common diagnosed cancer and cause of cancer-related death among men. This paper describes novel, deep learning based PCa CAD system that uses statistical central moments and Haralick features extracted from MR images, integrated with anamnestic data. Developed system has been trained on the dataset consisting of 330 lesions and evaluated on the challenge dataset using area under curve (AUC) related to estimated receiver operating characteristic (ROC). Two configurations of our method, based on statistical and Haralick features, scored 0.63 and 0.73 of AUC values. We draw conclusions from the challenge participation and discussed further improvements that could be made to the model to improve prostate classification.