  Among the different types of skin cancer, melanoma is considered to be the
deadliest and is difficult to treat at advanced stages. Detection of melanoma
at earlier stages can lead to reduced mortality rates. Desktop-based
computer-aided systems have been developed to assist dermatologists with early
diagnosis. However, there is significant interest in developing portable,
at-home melanoma diagnostic systems which can assess the risk of cancerous skin
lesions. Here, we present a smartphone application that combines image capture
capabilities with preprocessing and segmentation to extract the Asymmetry,
Border irregularity, Color variegation, and Diameter (ABCD) features of a skin
lesion. Using the feature sets, classification of malignancy is achieved
through support vector machine classifiers. By using adaptive algorithms in the
individual data-processing stages, our approach is made computationally light,
user friendly, and reliable in discriminating melanoma cases from benign ones.
Images of skin lesions are either captured with the smartphone camera or
imported from public datasets. The entire process from image capture to
classification runs on an Android smartphone equipped with a detachable 10x
lens, and processes an image in less than a second. The overall performance
metrics are evaluated on a public database of 200 images with Synthetic
Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88%
accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity,
95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics
and computation times are comparable or better than previous methods. This
all-inclusive smartphone application is designed to be easy-to-download and
easy-to-navigate for the end user, which is imperative for the eventual
democratization of such medical diagnostic systems.
