Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems
Abstract
:1. Introduction
2. Materials and Methods
2.1. Hyperspectral Skin Cancer Database
2.2. Hyperspectral Dermatologic Classification Framework
2.2.1. Pre-Processing Chain
Algorithm 1. Pre-processing chain. |
Input:Y → Hyperspectral image with n pixels and b bands →Dark reference →White reference →number of neighbours
|
2.2.2. Automatic PSL Segmentation
Algorithm 2. Automatic segmentation. |
Input: Y, k,, HUGE_VAL
|
2.2.3. Supervised Classification
Algorithm 3. Supervised classification. |
Input:pix_no_skin, npix_no_skin, class, nsv, sv, epsilon
|
3. Parallel Classification Pipelines
3.1. Parallel Pre-Processing Versions
3.2. Parallel K-Means Versions
3.3. Parallel Support Vector Machine Versions
3.4. Complete Classification System
4. Experimental Results and Discussion
4.1. Skin Cancer Classification Performance
4.2. Real-Time Elaboration
4.3. Comparison and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Pre-Processing | K-Means | SVM | |||||||
---|---|---|---|---|---|---|---|---|---|
S | O | C | C | S | O | C1 | C2 | C3 | |
V1 | × | × | × | ||||||
V2 | × | × | × | ||||||
V3 | × | × | × | ||||||
V4 | × | × | × | ||||||
V5 | × | × | × | ||||||
V6 | × | × | × | ||||||
V7 | × | × | × | ||||||
V8 | × | × | × | ||||||
V9 | × | × | × | ||||||
V10 | × | × | × | ||||||
V11 | × | × | × | ||||||
V12 | × | × | × | ||||||
V13 | × | × | × | ||||||
V14 | × | × | × | ||||||
V15 | × | × | × |
Classifier | Hyperparameters | FoM (%) |
---|---|---|
SVM Linear | 38.82 | |
SVM RBF | ; | 29.98 |
SVM Sigmoid | ; ; | 60.67 |
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Torti, E.; Leon, R.; La Salvia, M.; Florimbi, G.; Martinez-Vega, B.; Fabelo, H.; Ortega, S.; Callicó, G.M.; Leporati, F. Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems. Electronics 2020, 9, 1503. https://doi.org/10.3390/electronics9091503
Torti E, Leon R, La Salvia M, Florimbi G, Martinez-Vega B, Fabelo H, Ortega S, Callicó GM, Leporati F. Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems. Electronics. 2020; 9(9):1503. https://doi.org/10.3390/electronics9091503
Chicago/Turabian StyleTorti, Emanuele, Raquel Leon, Marco La Salvia, Giordana Florimbi, Beatriz Martinez-Vega, Himar Fabelo, Samuel Ortega, Gustavo M. Callicó, and Francesco Leporati. 2020. "Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems" Electronics 9, no. 9: 1503. https://doi.org/10.3390/electronics9091503
APA StyleTorti, E., Leon, R., La Salvia, M., Florimbi, G., Martinez-Vega, B., Fabelo, H., Ortega, S., Callicó, G. M., & Leporati, F. (2020). Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems. Electronics, 9(9), 1503. https://doi.org/10.3390/electronics9091503