1 Classification of Airspora Using Support Vector Machines (SVM) 82 ...

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K. Dorsey, E. Levetin: Biological Science, University of Tulsa, Tulsa, OK. RATIONALE: Airborne fungal spores are well known allergens. Although it is generally ...
J ALLERGY CLIN IMMUNOL VOLUME 111, NUMBER 2

Abstracts

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1 Classificationof AirsporaUsing SupportVector Machines (SVM)

3 AConcentrations Comparison of Counting Methods for Hourly Airborne Spore

S. Kumar, S. H. Ong, S. Ranganath, T. C. Ong, F. T. Chew; National University of Singapore, Singapore, SINGAPORE. RATIONALE: Manual quantification of airspora is a slow and laborious process. Automation of this process would not only make it faster and less laborious, it may be more amendable to larger number of slides/images, and increase accuracy and consistency. Computer based classification is seen as a vital step in the automation of airspora counts via image analysis. METHODS: We evaluated the performance of two types of Support Vector Machines, SVM (polynomial or gaussian), against statistical methods such as linear discriminants and muhilayer perceptrons (MLP), to classify airspora. A data set of eleven types of airspora common to the tropical Southeast Asian region, comprising fungal spores (Curvularia sp., Dreschlera spp., Di~,mosphaeria sp., Pithomyces sp.), fern spores (Asplenium nidus, Nephrolepis auriculata, Stenochlaena palustris) and pollen (Acacia sp., Casuarina equisetifolia, Elaeis guineensis, Podocarpus sp./Pinus) was analyzed. Each data has 20 size and shape features and one textural feature (roughness). 4500 specimens were evaluated. Approximately 1500 were used for training, while the remainder was used as the "unseen" set to test the classification performance. RESULTS: Polynomial and gaussian SVM give an overall classification accuracy of 94.3% and 94.0%, respectively, on unseen airspora data sets compared to 93.2% by MLP and 89.1% by linear discriminants. We observed that linear discriminants give poor accuracy since distribution of the labeled data set is non-linearly separable. MLP, though comparable, required additional heuristics based techniques to ensure its performance on unseen data is satisfactory. CONCLUSIONS: SVM gives better classification accuracy on unseen airspora data sets compared to MLP and statistical linear discriminant.

L. Lindley ],2, E. Levetin2; t Biology, Northeastern State University, Tahlequah, OK, 2Biological Science, University of Tulsa. Tulsa, OK. RATIONALE: Air sampling can be a valuable tool for allergists, who may advise patients to stay indoors on days with high pollen or spore levels. Outdoor air sampling is commonly done with a Burkard Spore Trap. Two counting methods are generally used: the single longitudinal traverse method (SLM) and the twelve transverse traverse method (TTM). SLM is fast and provides a good approximation of the average daily spore concentration. TTM takes more time, but it gives a better approximation of the average daily concentration and senses diurnal rhythm. However, TTM is too time-consuming for routine use. The current project was undertaken to find a counting method that shows changes in spore concentrations during the day but takes less time than the TTM. METHODS: The 28-fields method is a variant of TTM in which only 28 fields are counted per traverse. To test this method, the twelve traverses per slide were counted twice. For each traverse, the entire traverse was counted, then 28 fields were recounted. The bihourly spore concentrations were then calculated and compared statistically using the Wilcoxon paired-sample test and Spearman correlation. RESULTS: Concentrations obtained from counting 28 fields correlated well (p