Multi-scoring Feature selection method based on SVM-RFE for prostate cancer diagnosis

Prostate cancer diagnosis is based mainly by microscopic evaluation of prostate tissue biopsy which includes assigning cancer grading. The latter is crucial in evaluating the prognosis or cancer progression and treatment. The common grading system used is Gleason grading system that classifies the prostate cancer into five basic grades based on the architecture and pattern of glandular proliferation. However, this process may be subjected to inter and intra observer variation. Therefore, the main aim of this paper is to develop a computer aided diagnosis (CAD) utilizing supervised machine learning techniques for Gleason grading of prostate histology. The proposed procedure utilizes the main tissue components of the images in an ensemble style to correctly classify the input histopathological image into benign or malignant. Moreover, the texture features of the benign and malignant images can be used to build the proposed ensemble framework. However, not all extracted texture features contribute to the improvement of the classification performance of the proposed ensemble framework. Therefore, to select the more informative features from a set is a critical issue. In this study, a new multi-scoring features selection method based on SVM-RFE and conditional mutual information (CMI) is proposed.