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…
A Fuzzy Cognitive Map learning approach for coronary artery disease diagnosis in Nuclear Medicine Abstract Coronary artery disease (CAD) is the primary cause of death and chronic disability among cardiovascular conditions worldwide.…
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…
A Medical Decision Support System for the Prediction of the Coronary Artery Disease Using Fuzzy Cognitive Maps Abstract There is a lot discussion nowadays regarding the decision-making problem. The Making decisions and creating computational models…
Deep learning for the detection and localization of abnormal parathyroid glands in patients with hyperparathyroidism Abstract Background: Preoperative imaging methods for the localization of abnormal parathyroid glands are widely used to facilitate ensuing…
Fuzzy Cognitive Maps and Explainable Artificial Intelligence: a critical perspective Abstract There is a lot of discussion regarding the interpretability and explainability of modern artificial intelligence methodologies, especially…
Evaluation of Grad-CAM for explaining Deep Learning’s decisions on various medical imaging datasets Abstract Deep Learning (DL) is a well-established pipeline for feature extraction in medical and non-medical imaging tasks, such…
Intelligent Modeling for the food chain using Fuzzy Cognitive Maps Abstract The development of a methodological framework for evaluating the quality of a “product” on a food chain…
Explainable Deep Learning for localising abnormal Parathyroid Glands in parathyroid scintigraphy Abstract Parathyroid scintigraphy with 99mTc-sestamibi (MIBI) is an established technique for localising abnormal Parathyroid Glands (PGs). However, the…
Explainable YOLOv8 model for Solitary Pulmonary Nodules Classification using Positron Emission Tomography and Computed Tomography Scans
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…
Artificial Intelligence Algorithms for Epiretinal Membrane Detection, Segmentation and Postoperative BCVA Prediction: A Systematic Review and Meta-Analysis Epiretinal membrane (ERM) is a common retinal pathology associated with progressive visual impairment, requiring timely and accurate assessment.…
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…
Neural-FCM: A Deep Learning Approach for Weight Matrix Optimization in Fuzzy Cognitive Map Classifiers Abstract The demand for interpretable and accurate machine learning models continues to grow, especially in critical domains. The…
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…
Explainable deep fuzzy cognitive map diagnosis of coronary artery disease: Integrating myocardial perfusion imaging, clinical data, and natural language insights
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 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
Deep Learning methods to reveal important X-Ray features in COVID-19 detection: investigation of explainability and fea-ture reproducibility
An explainable classification method of SPECT myocardial perfusion images in nuclear cardiology using deep learning and Grad-Cam
Modeling the spread of dangerous pandemics with the utilization of a Hybrid Statistical- Advanced-Fuzzy-Cognitive-Map algorithm: the example of COVID-19
Advanced fuzzy cognitive maps: state space and rule-based methodology for coronary artery disease detection
Non – invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps
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
