Segmentation-Based Fractal Texture Analysis and Deep Convolution Networks for Multigrain Scoring of Prostate Cancer

Abstract

Prostate cancer diagnosis and staging is of paramount importance to effective treatment planning and better prognosis. Computer aided diagnosis and staging can contribute to improving and speeding up these stages, especially with the advances of deep learning. The International Society of Urological Pathology (ISUP) grading of stained Whole Slide histopathological Images (WSIs) can be considered the gold standard for grading. However, WSIs suffer from large size and wide background areas which hinder the learning process. Hence, a segmentation-based fractal analysis approach is applied to address this issue and elect relevant patches to be input to the learning algorithm. EfficientNet Convolution Neural Network (CNN) achieves a promising accuracy of 80.7% and Quadratic Weighted Kappa (QWK) of 95.4%. The proposed approach remedies the size problem of WSIs and improves the grading accuracy using light weight learning models.