Classifying Melanoma and Seborrheic Keratosis Automatically with Polarization Speckle Imaging Skin cancer is the most common cancer in western countries with a high incidence rate. Among all different types of skin cancers, malignant melanoma is the most fatal but has a promising prognosis if it is detected and treated at the early stages. However, melanoma often resemble to seborrheic keratosis (SK), a benign skin condition, and cause misdiagnosis. Therefore, it is important to develop a framework with computer aided system and noninvasive techniques to assist in the clinical diagnosis of melanoma. In this study, we extend a recent polarization speckle imaging method based on depolarization rate and achieved automatic detection of melanoma by leveraging the power of machine learning strategies. We collected 143 malignant melanoma and seborrheic keratosis lesions. Different machine learning methods, including support vector machine, random forest and knearest neighbor, were employed for the classification between melanoma and seborrheic keratosis. In order to explore the impact of different light sources, we further compared the classification performance of depolarization rate with blue and red light sources using different classifiers. The results suggested that the most reliable classification performance was achieved by support vector machine, yielding a high accuracy of 86.31% and the most balanced performance between sensitivity and specificity. In addition, the depolarization rate with the blue light source demonstrated a consistently better performance than that with the red light source across different methods. Our promising classification performance shows evidence for the potentials of computer aided diagnosis of melanoma with polarization speckle imaging, providing an additional noninvasive in vivo tool for skin cancer detection which could benefit future clinical dermatology research.

Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.