Forecasting PM2.5 and NO2 Concentrations in Patras Using Low-Cost Sensors and Machine Learning Abstract: We present a machine learning methodology for forecasting next day’s PM2.5 and NO2 concentrations in Patras, Greece,…
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…
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…
Towards an Internet of Things application for the prognosis of Coronary Artery Disease using Machine Learning and Fuzzy Logic Abstract Internet of Things (IoT) is a field of growing interest and reputation. It is a result of…
The Role of Artificial Intelligence in the Diagnosis, Segmentation, and Prediction of Retinal Vein Occlusion: A Systematic Review Abstract Retinal vein occlusion (RVO) is the second most common cause of vision loss after diabetic retinopathy. It…
Improving Real-time High-Resolution Estimates of PM2.5 Concentration Fields in Urban Areas by the SmartAQ+ System with Data Fusion and Machine Learning Abstract Monitoring PM2.5 (mass of particles with diameter less than 2.5 μm) concentrations is challenging due to the limited number…
A Review on SPECT Myocardial Perfusion Imaging Attenuation Correction using Deep Learning Abstract Attenuation correction (AC) is an essential process in Single Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging…
Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis Abstract Geographic atrophy (GA) is a progressive retinal disease associated with late-stage age-related macular degeneration (AMD), a significant…
Monitoring of Indoor Air Quality in a Classroom Combining a Low-Cost Sensor System and Machine Learning Abstract Monitoring indoor air quality in schools is essential, particularly as children are highly vulnerable to air pollution.…
Utility of disease probability scores to guide decision-making during screening for phaeochromocytoma and paraganglioma: a machine learning modelling cross sectional study Summary Background Interpretation of plasma metanephrines and methoxytyramine to assess likelihood of phaeochromocytoma/paraganglioma (PPGL) during screening can be…
Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis
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
Prediction of the concentration and source contributions of PM2.5 and gas-phase pollutants in an urban area with the SmartAQ forecasting system
Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET CT Screening
Calibration and inter-unit consistency assessment of an electrochemical sensor system using machine learning
Explainable deep fuzzy cognitive map diagnosis of coronary artery disease: Integrating myocardial perfusion imaging, clinical data, and natural language insights
Innovative attention-based explainable feature-fusion VGG19 network for characterising Myocardial Perfusion Imaging SPECT Polar Maps in patients with suspected Coronary Artery Disease
Explainable Artificial Intelligence Method (ParaNet+) Localises Abnormal Parathyroid Glands in Scintigraphic Scans of Patients with Primary Hyperparathyroidism
Classification Models for Assessing Coronary Artery Disease Instances Using Clinical and Biometric Data: A man-in-the-loop approach
Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models
An attention-based Deep Convolutional Neural Network for brain tumour and disorder classification and grading in Magnetic Resonance Imaging
Field calibration of a low-cost Air Quality monitoring device in a southeastern European site using Machine Learning models
An explainable Deep Learning framework for detecting and localising smoke and fire incidents: evaluation of Grad-CAM++ and LIME
AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: A review
Artificial Intelligence methods for identifying and localizing abnormal parathyroid glands: A review study
A Deep Learning methodology for the detection of abnormal Parathyroid Glands from scintigraphy with 99mTc-sestamibi
An explainable classification method of SPECT myocardial perfusion images in nuclear cardiology using deep learning and Grad-Cam
Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review
Deep Learning methods to reveal important X-Ray features in COVID-19 detection: investigation of explainability and fea-ture reproducibility
Industrial object and defect recognition utilizing multilevel feature extraction from industrial scenes with Deep Learning approach
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
Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation
Multi-Input Deep Learning Approach for Cardiovascular Disease Diagnosis using Myocardial Perfusion Imaging and Clinical Data
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases
Improving Real-time High-Resolution Estimations of PM2.5 Concentration Fields for the present time in Urban Areas by the SmartAQ+ System with Data Fusion and Machine Learning
Development and calibration of an Air Quality Monitoring (AQM) appliance based on low-cost electrochemical and laser sensors
Machine Learning in Air Quality: Overview of applications and a case study on the SmartAQ forecasting system
