Lorenzo Putzu

981 total citations
28 papers, 590 citations indexed

About

Lorenzo Putzu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics. According to data from OpenAlex, Lorenzo Putzu has authored 28 papers receiving a total of 590 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 17 papers in Artificial Intelligence and 9 papers in Biophysics. Recurrent topics in Lorenzo Putzu's work include Digital Imaging for Blood Diseases (14 papers), AI in cancer detection (12 papers) and Cell Image Analysis Techniques (9 papers). Lorenzo Putzu is often cited by papers focused on Digital Imaging for Blood Diseases (14 papers), AI in cancer detection (12 papers) and Cell Image Analysis Techniques (9 papers). Lorenzo Putzu collaborates with scholars based in Italy. Lorenzo Putzu's co-authors include Cecilia Di Ruberto, Giovanni Caocci, Andrea Loddo, Giorgio Fumera, G. Rodríguez, Luca Piras, Giorgio Giacinto, Gianni Fenu, Fabio Roli and Simone Porcu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Pattern Recognition.

In The Last Decade

Lorenzo Putzu

25 papers receiving 556 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lorenzo Putzu Italy 13 525 284 147 141 115 28 590
Khamael Al-Dulaimi Australia 11 206 0.4× 150 0.5× 71 0.5× 60 0.4× 86 0.7× 22 327
Roopa B. Hegde India 8 332 0.6× 215 0.8× 84 0.6× 144 1.0× 63 0.5× 17 416
Luis H. S. Vogado Brazil 9 325 0.6× 235 0.8× 65 0.4× 167 1.2× 67 0.6× 22 439
Madhumala Ghosh India 10 347 0.7× 131 0.5× 118 0.8× 61 0.4× 90 0.8× 14 411
Harishchandra Hebbar India 7 336 0.6× 213 0.8× 84 0.6× 147 1.0× 62 0.5× 13 390
Vasundhara Acharya India 11 158 0.3× 131 0.5× 39 0.3× 148 1.0× 81 0.7× 25 417
Laura Boldú Spain 9 359 0.7× 251 0.9× 99 0.7× 141 1.0× 31 0.3× 13 455
Sarmad Shafique Pakistan 5 291 0.6× 202 0.7× 50 0.3× 116 0.8× 56 0.5× 9 339
G. Gopakumar India 9 157 0.3× 77 0.3× 57 0.4× 51 0.4× 46 0.4× 32 290
Kangkana Bora India 11 159 0.3× 302 1.1× 22 0.1× 158 1.1× 83 0.7× 31 511

Countries citing papers authored by Lorenzo Putzu

Since Specialization
Citations

This map shows the geographic impact of Lorenzo Putzu'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 Lorenzo Putzu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lorenzo Putzu more than expected).

Fields of papers citing papers by Lorenzo Putzu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lorenzo Putzu. 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 Lorenzo Putzu. The network helps show where Lorenzo Putzu may publish in the future.

Co-authorship network of co-authors of Lorenzo Putzu

This figure shows the co-authorship network connecting the top 25 collaborators of Lorenzo Putzu. A scholar is included among the top collaborators of Lorenzo Putzu 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 Lorenzo Putzu. Lorenzo Putzu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Putzu, Lorenzo, Simone Porcu, & Andrea Loddo. (2025). Distributed collaborative machine learning in real-world application scenario: A white blood cell subtypes classification case study. Image and Vision Computing. 162. 105673–105673.
2.
Putzu, Lorenzo, et al.. (2024). Synthetic Data for Video Surveillance Applications of Computer Vision: A Review. International Journal of Computer Vision. 132(10). 4473–4509. 5 indexed citations
3.
Loddo, Andrea, et al.. (2024). Federated Learning for Enhanced Cell Nuclei Segmentation in Histopathological Images. UNICA IRIS Institutional Research Information System (University of Cagliari). 4507–4516.
4.
Putzu, Lorenzo, et al.. (2023). Human-in-the-loop cross-domain person re-identification. Expert Systems with Applications. 226. 120216–120216. 12 indexed citations
5.
Ruberto, Cecilia Di, Andrea Loddo, & Lorenzo Putzu. (2023). On The Potential of Image Moments for Medical Diagnosis. Journal of Imaging. 9(3). 70–70. 3 indexed citations
6.
Putzu, Lorenzo, et al.. (2021). Scene-specific crowd counting using synthetic training images. Pattern Recognition. 124. 108484–108484. 18 indexed citations
7.
Loddo, Andrea & Lorenzo Putzu. (2021). On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario. SHILAP Revista de lepidopterología. 2(3). 394–412. 14 indexed citations
8.
Putzu, Lorenzo, et al.. (2020). Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios. UNICA IRIS Institutional Research Information System (University of Cagliari). 365–372. 2 indexed citations
9.
Putzu, Lorenzo, Luca Piras, & Giorgio Giacinto. (2020). Convolutional neural networks for relevance feedback in content based image retrieval. Multimedia Tools and Applications. 79(37-38). 26995–27021. 29 indexed citations
10.
Ruberto, Cecilia Di, Andrea Loddo, & Lorenzo Putzu. (2019). Detection of red and white blood cells from microscopic blood images using a region proposal approach. Computers in Biology and Medicine. 116. 103530–103530. 57 indexed citations
11.
Porcu, Simone, Andrea Loddo, Lorenzo Putzu, & Cecilia Di Ruberto. (2018). White Blood Cells Counting Via Vector Field Convolution Nuclei Segmentation. UNICA IRIS Institutional Research Information System (University of Cagliari). 5 indexed citations
12.
Loddo, Andrea, Lorenzo Putzu, Cecilia Di Ruberto, & Gianni Fenu. (2016). A Computer-Aided System for Differential Count from Peripheral Blood Cell Images. UNICA IRIS Institutional Research Information System (University of Cagliari). 112–118. 12 indexed citations
13.
Putzu, Lorenzo. (2016). Computer aided diagnosis algorithms for digital microscopy. UNICA IRIS Institutional Research Information System (University of Cagliari). 1 indexed citations
14.
Ruberto, Cecilia Di, Andrea Loddo, & Lorenzo Putzu. (2016). A leucocytes count system from blood smear images. Machine Vision and Applications. 27(8). 1151–1160. 33 indexed citations
15.
Putzu, Lorenzo, Giovanni Caocci, & Cecilia Di Ruberto. (2014). Leucocyte classification for leukaemia detection using image processing techniques. Artificial Intelligence in Medicine. 62(3). 179–191. 201 indexed citations
16.
Ruberto, Cecilia Di & Lorenzo Putzu. (2014). Accurate Blood Cells Segmentation through Intuitionistic Fuzzy Set Threshold. UNICA IRIS Institutional Research Information System (University of Cagliari). 35. 57–64. 15 indexed citations
17.
Ruberto, Cecilia Di & Lorenzo Putzu. (2014). A Fast Leaf Recognition Algorithm based on SVM Classifier and High Dimensional Feature Vector. UNICA IRIS Institutional Research Information System (University of Cagliari). 601–609. 20 indexed citations
18.
Putzu, Lorenzo & Cecilia Di Ruberto. (2013). White Blood Cells Identication and Classication from Leukemic Blood Image.. 99–106. 1 indexed citations
19.
Putzu, Lorenzo & Cecilia Di Ruberto. (2013). White Blood Cells Identification and Classification from Leukemic Blood Image. UNICA IRIS Institutional Research Information System (University of Cagliari). 99–106. 36 indexed citations
20.
Putzu, Lorenzo & Cecilia Di Ruberto. (2013). White Blood Cells Identification and Counting from Microscopic Blood Images. UNICA IRIS Institutional Research Information System (University of Cagliari). 73. 363–370. 14 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.

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