Prostate Cancer Gleason Grading: A Review on Deep Learning Approaches for Recognizing

Abstract

This survey paper is motivated by the imperative need for advanced and precise diagnostic tools in the realm of prostate cancer, where Gleason grading plays a pivotal role in determining the severity and treatment strategy. The aim of this comprehensive review is to explore and assess the diverse spectrum of deep learning approaches applied to prostate cancer Gleason grading, with a specific focus on convolutional neural networks (CNNs), transfer learning, ensemble methods, and emerging techniques. The primary contribution lies in offering a consolidated understanding of the current state-of-the-art methodologies, their architectures, and training strategies, while also addressing challenges and advancements in the integration of deep learning into clinical workflows. Furthermore, the survey discusses recent developments such as the incorporation of multimodal data and explainable AI methods, shedding light on their potential to enhance the interpretability and adoption of deep learning models in the critical domain of prostate cancer diagnosis. Through this, the paper aims to provide a valuable resource for researchers, clinicians, and practitioners, guiding future endeavors toward more accurate and efficient Gleason grading using deep learning techniques.