Deep learning approach to identifying cancer subtypes using convolutional hyperbolic k nearest neighbours method

Abstract

ABSTRACT To properly treat a cancer patient, radiologists must first review a large volume of cancer images. Based on this information, the appropriate treatment is prescribed. However, the manual cancer image classification is susceptible to errors due to the intricate nature of different tumours. This study introduces a novel convolutional hyperbolic k-nearest neighbours (ChkNN) method to address this challenge. Using deep learning algorithms, convolutional neural networks (CNN), hyperbolic mapping, and hyperbolic geometry, the proposed method effectively extracts and embeds features in hyperbolic space. This embedding not only enhances classification accuracy rates up to 99.92% but also facilitates informed decision-making in cancer diagnosis. The ChkNN method holds immense potential for radiologists worldwide, empowering them to make more accurate and timely treatment decisions, ultimately improving patient outcomes.