Francesco Prinzi

Orcid: 0000-0002-8152-3297

According to our database1, Francesco Prinzi authored at least 19 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Using AI explainable models and handwriting/drawing tasks for psychological well-being.
Inf. Syst., 2025

Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics.
Comput. Methods Programs Biomed., 2025

Machine Learning Algorithms for Biomedical Image Analysis and Their Applications.
Algorithms, 2025

A Local-to-Global Graph Neural Network for Major Depressive Disorder Classification from rs-fMRI.
Proceedings of the International Joint Conference on Neural Networks, 2025

2024
A Yolo-Based Model for Breast Cancer Detection in Mammograms.
Cogn. Comput., January, 2024

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.
Medical Image Anal., 2024

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.
J. Imaging Inform. Medicine, 2024

Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences.
Expert Syst. Appl., 2024

MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI.
Proceedings of the International Joint Conference on Neural Networks, 2024

2023
CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease.
Cogn. Comput., January, 2023

Transformer-Based Approach to Melanoma Detection.
Sensors, 2023

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images.
J. Imaging, 2023

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.
CoRR, 2023

Explainable Machine-Learning Models for COVID-19 Prognosis Prediction Using Clinical, Laboratory and Radiomic Features.
IEEE Access, 2023

Explainable Depression Detection Using Handwriting Features.
Proceedings of the Advanced Neural Artificial Intelligence: Theories and Applications, 2023

ViT-Based Classification of Mammogram Images: Impact of Data Augmentation Techniques.
Proceedings of the Advanced Neural Artificial Intelligence: Theories and Applications, 2023

Breast Cancer Malignancy Prediction through Explainable Models based on a Multimodal Signature of Features.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2023

Leveraging Diffuser Data Augmentation to Enhance ViT-Based Performance on Dermatoscopic Melanoma Images Classification.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2023

2022
ML-Based Radiomics Analysis for Breast Cancer Classification in DCE-MRI.
AII, 2022


  Loading...