Research Article
m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine
@INPROCEEDINGS{10.1007/978-3-319-49655-9_57, author={Muhammad Taufiq and Nazia Hameed and Adeel Anjum and Fozia Hameed}, title={m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine}, proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers}, proceedings_a={EHEALTH360}, year={2017}, month={1}, keywords={Skin cancer Melanoma Computer aided systems Mobile application Health care systems Machine learning}, doi={10.1007/978-3-319-49655-9_57} }- Muhammad Taufiq
Nazia Hameed
Adeel Anjum
Fozia Hameed
Year: 2017
m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine
EHEALTH360
Springer
DOI: 10.1007/978-3-319-49655-9_57
Abstract
Early detection of skin cancer is very important as it is one of the dangerous form of cancer spreading vigorously among humans. With the advancement of mobile technology; mobile enabled skin cancer detection systems are really demanding but currently very few real time skin cancer detection systems are available for general public and mostly available are the paid. In this paper authors proposed a real time mobile enabled health care system for the detection of skin melanoma for general users. Proposed system is developed using computer vision and image processing techniques. Noise is removed by applying the Gaussian filter. For segmentation Grab Cut algorithm is used. Support Vector Machine (SVM) is applied as a classification technique on the texture features like area, perimeter, eccentricity etc. The sensitivity and specificity rate achieved by the m-Skin Doctor is 80% and 75% respectively. The average time consumed by the application for classifying one image is 14938 ms.