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. 2011 Mar 30:2011:133-136.
doi: 10.1109/ISBI.2011.5872372.

SkinScan©: A PORTABLE LIBRARY FOR MELANOMA DETECTION ON HANDHELD DEVICES

Tarun Wadhawan  1 , Ning Situ, Keith Lancaster, Xiaojing Yuan, George Zouridakis
Affiliations

SkinScan©: A PORTABLE LIBRARY FOR MELANOMA DETECTION ON HANDHELD DEVICES

Tarun Wadhawan et al. Proc IEEE Int Symp Biomed Imaging. .

Abstract

We have developed a portable library for automated detection of melanoma termed SkinScan© that can be used on smartphones and other handheld devices. Compared to desktop computers, embedded processors have limited processing speed, memory, and power, but they have the advantage of portability and low cost. In this study we explored the feasibility of running a sophisticated application for automated skin cancer detection on an Apple iPhone 4. Our results demonstrate that the proposed library with the advanced image processing and analysis algorithms has excellent performance on handheld and desktop computers. Therefore, deployment of smartphones as screening devices for skin cancer and other skin diseases can have a significant impact on health care delivery in underserved and remote areas.

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Figures

Fig. 1
Fig. 1
Lesion classification: select a patch from the lesion, apply a 3-level Haar wavelet transform, extract texture features, build a histogram using the cluster centers obtained during training, and input histogram to trained SVM classifier to classify the lesion.
Fig. 2
Fig. 2
SkinScan application on Apple iPhone™ device
Fig. 3
Fig. 3
Error ratio distribution of three segmentation methods on the dataset of 1300 skin lesion images. The red dotted line marks the threshold for correct segmentation.
Fig. 4
Fig. 4
ROC curve for lesion classification.
See this image and copyright information in PMC

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