Lucia Cavallaro
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Sociology and Political Science
- Computer Vision and Pattern Recognition
- Neurology
- Co-authors
- Antonio LiottaNajah AlsubaieNazik AlturkiHanan AljuaidPasquale De MeoOvidiu BagdasarGiacomo FiumaraWei Song
- Topics
- Complex Network Analysis Techniques (4 papers)Radiomics and Machine Learning in Medical Imaging (2 papers)Crime Patterns and Interventions (2 papers)
- Journals
- PLoS ONEComputer Methods and Programs in BiomedicineBiomedical Signal Processing and Control
- Partner nations
- ItalyUnited KingdomNetherlands
In The Last Decade
Lucia Cavallaro
8 papers receiving 219 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 126
- Radiology, Nuclear Medicine and Imaging 91
- Sociology and Political Science 44
- Computer Vision and Pattern Recognition 33
- Neurology 32
Countries citing papers authored by Lucia Cavallaro
This map shows the geographic impact of Lucia Cavallaro's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lucia Cavallaro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lucia Cavallaro more than expected).
Fields of papers citing papers by Lucia Cavallaro
This network shows the impact of papers produced by Lucia Cavallaro. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lucia Cavallaro. The network helps show where Lucia Cavallaro may publish in the future.
Co-authorship network of co-authors of Lucia Cavallaro
This figure shows the co-authorship network connecting the top 25 collaborators of Lucia Cavallaro. A scholar is included among the top collaborators of Lucia Cavallaro based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lucia Cavallaro. Lucia Cavallaro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 5 | |
| 5 | Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learningbreakdown → | 139 |
| 6 | 21 | |
| 7 | 3 | |
| 8 | 5 | |
| 9 | 53 |
About Lucia Cavallaro
Lucia Cavallaro is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 9 papers that have together received 230 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (4 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Crime Patterns and Interventions (2 papers). The work is most often cited by research in Artificial Intelligence (126 citations), Radiology, Nuclear Medicine and Imaging (91 citations) and Neurology (32 citations). Lucia Cavallaro has collaborated with scholars based in Italy, United Kingdom and Netherlands. Frequent co-authors include Antonio Liotta, Najah Alsubaie, Nazik Alturki, Hanan Aljuaid, Pasquale De Meo, Ovidiu Bagdasar, Giacomo Fiumara, Wei Song, Salvatore Catanese and Giovanni Stilo. Their work appears in journals such as PLoS ONE, Computer Methods and Programs in Biomedicine and Biomedical Signal Processing and Control.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.