Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Cancer Diagnosis Using Deep Learning: A Bibliographic Review
2019278 citationsKhushboo Munir, Hassan Elahi et al.Cancersprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Fabrizio Frezza
Since
Specialization
Citations
This map shows the geographic impact of Fabrizio Frezza'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 Fabrizio Frezza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabrizio Frezza more than expected).
This network shows the impact of papers produced by Fabrizio Frezza. 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 Fabrizio Frezza. The network helps show where Fabrizio Frezza may publish in the future.
Co-authorship network of co-authors of Fabrizio Frezza
This figure shows the co-authorship network connecting the top 25 collaborators of Fabrizio Frezza.
A scholar is included among the top collaborators of Fabrizio Frezza 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 Fabrizio Frezza. Fabrizio Frezza is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gori, F., R. Martı́nez-Herrero, Olga Korotkova, et al.. (2024). Affine diffractive beam dividers. Journal of the Optical Society of America A. 41(3). 510–510.3 indexed citations
Munir, Khushboo, et al.. (2019). Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers. 11(9). 1235–1235.278 indexed citations breakdown →
Frezza, Fabrizio, et al.. (2017). Determination of concrete cover thickness in a reinforced concrete pillar by observation of the scattered electromagnetic field. Institutional Research Information System (Università degli Studi di Brescia). 13784.1 indexed citations
15.
Pajewski, Lara, Raffaele Persico, Vincenzo Ferrara, & Fabrizio Frezza. (2016). Ground penetrating radar prototypes developed in COST action TU1208. IRIS Research product catalog (Sapienza University of Rome).1 indexed citations
16.
Frezza, Fabrizio, Lara Pajewski, Emanuele Piuzzi, Cristina Ponti, & Giuseppe Schettini. (2012). Advances in EBG-resonator antenna research. IRIS Research product catalog (Sapienza University of Rome). 1301–1304.6 indexed citations
17.
Marzano, Frank S., et al.. (2011). Free-space optical high-speed link in the urban area of southern Rome: Preliminary experimental set up and channel modelling. IRIS Research product catalog (Sapienza University of Rome). 2737–2741.6 indexed citations
18.
Frezza, Fabrizio, et al.. (2009). FDTD Simulation of GPR Measurements in a Laboratory Sandbox for Landmine Detection. 45–49.2 indexed citations
19.
Valerio, Guido, S. Paulotto, Paolo Baccarelli, et al.. (2009). Improving approximate 1D Bloch analysis through simulation of truncated periodic structures. IRIS Research product catalog (Sapienza University of Rome). 3453–3456.1 indexed citations
20.
Baccarelli, Paolo, Paolo Burghignoli, Fabrizio Frezza, et al.. (2004). NEW DISPERSION CHARACTERISTICS AND SURFACE-WAVE SUPPRESSION IN DOUBLE-NEGATIVE METAMATERIAL GROUNDED SLABS. IRIS Research product catalog (Sapienza University of Rome). 379–381.1 indexed citations
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.