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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects
|
|---|---|
| Published in |
BioMedical Engineering OnLine, April 2014
|
| DOI | 10.1186/1475-925x-13-41 |
| Pubmed ID | |
| Authors |
Macedo Firmino, Antônio H Morais, Roberto M Mendoça, Marcel R Dantas, Helio R Hekis, Ricardo Valentim |
| Abstract |
The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 43% |
| Brazil | 2 | 29% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 180 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| United States | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 176 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 35 | 19% |
| Student > Master | 31 | 17% |
| Researcher | 25 | 14% |
| Student > Bachelor | 18 | 10% |
| Professor > Associate Professor | 9 | 5% |
| Other | 27 | 15% |
| Unknown | 35 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 40 | 22% |
| Engineering | 38 | 21% |
| Medicine and Dentistry | 24 | 13% |
| Social Sciences | 4 | 2% |
| Agricultural and Biological Sciences | 4 | 2% |
| Other | 22 | 12% |
| Unknown | 48 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2019.
All research outputs
#5,240,151
of 25,373,627 outputs
Outputs from BioMedical Engineering OnLine
#125
of 867 outputs
Outputs of similar age
#48,927
of 241,522 outputs
Outputs of similar age from BioMedical Engineering OnLine
#4
of 25 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 241,522 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.