Name
Title | ||
|---|---|---|
An accelerated framework for the classification of biological targets from solid-state micropore data. |
Abstract | ||
|---|---|---|
•Micro/nanoscale devices that detect biological targets especially for early cancer diagnosis generate large datasets.•Challenges like high background noise, slow analysis, and low signal-to-noise ratio make data analysis significantly tedious.•A novel algorithm is implemented on GPU that automates the rapid detection of biological targets in larger datasets.•The machine-learning approach records events, computes features and classifies future pulses into their respective types.•The approach detects cells with an accuracy of 70% and demonstrates a speedup of 3-4X over serial implementation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.cmpb.2016.06.001 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Cancer detection,Pattern detection and classification,Run-time systems,Human cells,Solid-state micropores/nanopores | Training set,Computer vision,Background noise,Computer science,Raw data,Cancer detection,Artificial intelligence,Graphics processing unit,Solid-state,Speedup | Journal |
Volume | Issue | ISSN |
134 | C | 0169-2607 |
Citations | PageRank | References |
1 | 0.48 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madiha Hanif | 1 | 1 | 0.48 |
| Abdul Hafeez | 2 | 5 | 1.63 |
| Yusuf Suleman | 3 | 1 | 0.48 |
| M. Mustafa Rafique | 4 | 157 | 15.49 |
| Ali R. Butt | 5 | 651 | 47.51 |
| Samir M. Iqbal | 6 | 4 | 2.95 |