Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system

BMC Medical Imaging

Research Article

Huabei Jiang¹  Qizhi Zhang¹  Lin Li²  Bruce H Thiers³  James Z Wang⁴  Yihua Ding⁴ 

[1] Department of Biomedical Engineering, University of Florida, 32611, Gainesville, FL, USA;Department of Computer Science & Software Engineering, Seattle University, 98122, Seattle, WA, USA;Department of Dermatology, Medical University of South Carolina, 29425, Charleston, SC, USA;School of Computing, Clemson University, 29634, Clemson, SC, USA;

关键词: Melanoma;    Artificial Neural Network;    Pixel Intensity;    Lesion Area;    Desktop Application;   

DOI  :  10.1186/1471-2342-14-36

 received in 2014-06-03, accepted in 2014-10-03,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundEarly and accurate diagnosis of melanoma, the deadliest type of skin cancer, has the potential to reduce morbidity and mortality rate. However, early diagnosis of melanoma is not trivial even for experienced dermatologists, as it needs sampling and laboratory tests which can be extremely complex and subjective. The accuracy of clinical diagnosis of melanoma is also an issue especially in distinguishing between melanoma and mole. To solve these problems, this paper presents an approach that makes non-subjective judgements based on quantitative measures for automatic diagnosis of melanoma.MethodsOur approach involves image acquisition, image processing, feature extraction, and classification. 187 images (19 malignant melanoma and 168 benign lesions) were collected in a clinic by a spectroscopic device that combines single-scattered, polarized light spectroscopy with multiple-scattered, un-polarized light spectroscopy. After noise reduction and image normalization, features were extracted based on statistical measurements (i.e. mean, standard deviation, mean absolute deviation, L1 norm, and L2 norm) of image pixel intensities to characterize the pattern of melanoma. Finally, these features were fed into certain classifiers to train learning models for classification.ResultsWe adopted three classifiers – artificial neural network, naïve bayes, and k-nearest neighbour to evaluate our approach separately. The naive bayes classifier achieved the best performance - 89% accuracy, 89% sensitivity and 89% specificity, which was integrated with our approach in a desktop application running on the spectroscopic system for diagnosis of melanoma.ConclusionsOur work has two strengths. (1) We have used single scattered polarized light spectroscopy and multiple scattered unpolarized light spectroscopy to decipher the multilayered characteristics of human skin. (2) Our approach does not need image segmentation, as we directly probe tiny spots in the lesion skin and the image scans do not involve background skin. The desktop application for automatic diagnosis of melanoma can help dermatologists get a non-subjective second opinion for their diagnosis decision.

【 授权许可】

CC BY   
© Li et al.; licensee BioMed Central Ltd. 2014