Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach

Unai Muñoz-Aseguinolaza; Izaro Fernandez-Iriondo; Itsaso Rodríguez-Moreno; Naiara Aginako; Basilio Sierra

doi:10.1016/j.heliyon.2023.e21203

Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach

Heliyon. 2023 Oct 20;9(11):e21203. doi: 10.1016/j.heliyon.2023.e21203. eCollection 2023 Nov.

Authors

Unai Muñoz-Aseguinolaza¹, Izaro Fernandez-Iriondo^{1

2}, Itsaso Rodríguez-Moreno¹, Naiara Aginako¹, Basilio Sierra¹

Affiliations

¹ Department of Computer Science and Artificial Intelligence, University of Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain.
² Computational Neuroimaging Lab, Biobizkaia Health Research Institute, Bilbao, Spain.

Abstract

Recent developments in technology and research have offered a wide variety of new techniques for image and data analysis within the medical field. Medical research helps doctors and researchers acquire not only knowledge about health and new diseases, but also techniques of prevention and treatment. In particular, radiomic analysis is mainly used to extract quantitative data from medical images and to build a model strong enough to diagnose focal diseases. However, finding a model capable to fit all patient situations is not an easy task. In this paper frame prediction models and classification models are reported in order to predict the evolution of a given data series and determine whether an anomaly exists or not. This article also shows how to build and make use of a convolutional neural network-based architecture aiming to accomplish prediction task for medical images, not only using common computer tomography scans, but also 3D volumes.

Keywords: 3D image analysis; Convolutional neural networks; Frame prediction models; Lung cancer detection; Medical image processing; Radiomic analysis.