Prediction of Malignancy in Lung Cancer Using Several Strategies for the Fusion of Multi-Channel Pyradiomics Images

EasyChair Preprint 10864

Abstract

This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.

Keyphrases: Lung Cancer, Radiomics, Screening, early diagnosis, nodule diagnosis, nodule diagnosis.

@booklet{EasyChair:10864,
  author    = {Guillermo Torres and Jan Rodríguez Dueñas and Sonia Baeza Mena and Antoni Rosell Gratacós and Carles Sanchez and Debora Gil},
  title     = {Prediction of Malignancy in Lung Cancer Using Several Strategies for the Fusion of Multi-Channel Pyradiomics Images},
  howpublished = {EasyChair Preprint 10864},
  year      = {EasyChair, 2023}}