Feature Engineering with Large Language Models Improves Solitary Pulmonary Nodule Malignancy Classification with Machine Learning Abstract: Accurate classification of solitary pulmonary nodules (SPNs) as benign or malignant can be critical for lung cancer…
Medical Decision Support System in Nuclear Medicine Diagnosis for Non-Small Cell Lung Cancer and Coronary Artery Disease: A First Stage Prototype Abstract Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. It is a complex…
A Medical Decision Support System (MDSS) for Explainable Multimodal Detection of Non-Small Cell Lung Cancer Using Clinical and PET Data Abstract Non-small cell lung cancer is a prevalent form of lung cancer, with Solitary Pulmonary Nodules (SPNs) as…
Multimodal Diagnosis using Deep Fuzzy Cognitive Map with Extreme Learning Machine Integrated into a Medical Decision Support System for Coronary Artery Disease and Non-Small Cell Lung Cancer Detection Abstract Early detection of Coronary Artery Disease (CAD) and Non-Small Cell Lung Cancer (NSCLC) is crucial for improving…
Deep Fuzzy Cognitive Map methodology for Non-Small Cell Lung Cancer diagnosis based on Positron Emission Tomography imaging Abstract Non-Small Cell Lung Cancer (NSCLC) constitutes the major cause of cancer deaths worldwide in both men and…
Explainable Classification for Non-Small Cell Lung Cancer based on Positron Emission Tomography features and clinical data Abstract Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. It is a complex…
Investigating the agreement with human readers and generalisation capabilities of a transfer learning approach for predicting the malignancy of Solitary Pulmonary Nodules in CT screening Abstract Lung cancer remains a leading global health issue. This research used artificial intelligence to enhance lung nodule…
Solitary Pulmonary Nodule malignancy classification utilising 3D features and semi-supervised Deep Learning Abstract The volumetric representation of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) imaging is mandatory, especially for…
Clinical validation of the EMERALD’s MDSS t the University Hospital of Patras On April 1st, 2025, the developed Medical Decision Support System (MDSS) was clinically validated at the University Hospital…
Explainable YOLOv8 model for Solitary Pulmonary Nodules Classification using Positron Emission Tomography and Computed Tomography Scans
A machine learning approach for determining solitary pulmonary nodule malignancy in patients undergoing PET/CT examination Abstract Non-small cell lung cancer (NSCLC) constitutes about 85% of all lung cancers and is a leading cause…
A Multi-modal Machine Learning methodology for predicting Solitary Pulmonary Nodule malignancy in patients undergoing PET CT examination
Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET CT Screening
Classification of Lung Nodule Malignancy in Computed Tomography Imaging utilising Generative Adversarial Networks and Semi-supervised Transfer Learning
Automatic Classification of Solitary Pulmonary Nodules in PET CT imaging employing Transfer Learning techniques
Fuzzy Cognitive Explainable Analytics for Translating Model Complexity in Nuclear Medical Diagnosis (EMERALD) EMERALD takes a unique, holistic approach to patient-specific predictive modeling and MDSS development by extracting and integrating knowledge…
