Name
Abstract | ||
|---|---|---|
Skin cancer is one of the most common cancers in the United States. As technological advancements are made, algorithmic diagnosis of skin lesions is becoming more important. In this paper, we develop algorithms for segmenting the actual diseased area of skin in a given image of a skin lesion, and for classifying different types of skin lesions pictured in a given image. The cores of the algorithms used were based in persistent homology, an algebraic topology technique that is part of the rising field of Topological Data Analysis (TDA). The segmentation algorithm utilizes a similar concept to persistent homology that captures the robustness of segmented regions. For classification, we design two families of topological features from persistence diagrams-which we refer to as persistence statistics and persistence curves, and use linear support vector machine as classifiers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BigData.2018.8622175 | 2018 IEEE International Conference on Big Data (Big Data) |
Keywords | Field | DocType |
skin disease image analysis,skin cancer,algorithmic diagnosis,skin lesion,algebraic topology technique,Topological Data Analysis,segmentation algorithm,persistence diagrams,persistence statistics,persistence curves,linear support vector machine | Topological data analysis,Topology,Algebraic topology,Skin lesion,Computer science,Segmentation,Support vector machine,Skin cancer,Persistent homology,Robustness (computer science) | Conference |
ISSN | ISBN | Citations |
2639-1589 | 978-1-5386-5036-3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Min Chung | 1 | 1 | 4.40 |
| Chuan-Shen Hu | 2 | 1 | 1.36 |
| Austin Lawson | 3 | 0 | 0.34 |
| Clifford Smyth | 4 | 24 | 6.91 |