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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system
|
|---|---|
| Published in |
BMC Medical Imaging, October 2014
|
| DOI | 10.1186/1471-2342-14-36 |
| Pubmed ID | |
| Authors |
Lin Li, Qizhi Zhang, Yihua Ding, Huabei Jiang, Bruce H Thiers, James Z Wang |
| Abstract |
Early and accurate diagnosis of melanoma, the deadliest type of skin cancer, has the potential to reduce morbidity and mortality rate. However, early diagnosis of melanoma is not trivial even for experienced dermatologists, as it needs sampling and laboratory tests which can be extremely complex and subjective. The accuracy of clinical diagnosis of melanoma is also an issue especially in distinguishing between melanoma and mole. To solve these problems, this paper presents an approach that makes non-subjective judgements based on quantitative measures for automatic diagnosis of melanoma. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 10% |
| Brazil | 1 | 10% |
| Germany | 1 | 10% |
| United States | 1 | 10% |
| Unknown | 6 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 80% |
| Scientists | 1 | 10% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 14% |
| Student > Ph. D. Student | 10 | 13% |
| Student > Bachelor | 8 | 10% |
| Student > Master | 8 | 10% |
| Other | 6 | 8% |
| Other | 13 | 16% |
| Unknown | 23 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 22% |
| Computer Science | 15 | 19% |
| Engineering | 10 | 13% |
| Agricultural and Biological Sciences | 2 | 3% |
| Social Sciences | 2 | 3% |
| Other | 8 | 10% |
| Unknown | 25 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 07 May 2015.
All research outputs
#4,991,241
of 23,881,329 outputs
Outputs from BMC Medical Imaging
#59
of 604 outputs
Outputs of similar age
#54,859
of 258,209 outputs
Outputs of similar age from BMC Medical Imaging
#3
of 9 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 604 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done particularly well, scoring higher than 93% 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 258,209 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 77% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.