Malignant melanoma detection by Bag-of-Features classification

Abstract

In this paper, we apply a Bag-of-Features approach to malignant melanoma detection based on epiluminescence microscopy imaging. Each skin lesion is represented by a histogram of codewords or clusters identified from a training data set. Classification results using Naive Bayes classification and Support Vector Machines are reported. The best performance obtained is 82.21% on a dataset of 100 skin lesion images. Furthermore, since in melanoma screening false negative errors have a much higher impact and associated cost than false positive ones, we use the Neyman-Pearson score in our model selection scheme.

Implementation of the 7-point checklist for melanoma detection on smart handheld devices

“…When the algorithm runs on a handheld device we want to reduce computation time, so we choose M = 10 for the grid size, K = 24 for patch size, and L = 200 as the number of clusters in the feature space. By exhaustive parameter exploration in our previous studies [13], we found that these parameters are reasonable settings.…”

“…Elbaum et al [12] used wavelet coefficients as texture descriptors in their skin cancer screening system MelaFind ® . Our previous work [13] has demonstrated the effectiveness of Haar wavelet coefficients and local binary patterns [14] for melanoma detection.…”

Mining textural knowledge in biological images: Applications, methods and trends

“…In the last few years, BoW framework was extensively applied to the automated categorisation of histopathological images [78], [79]. For example, in Ref, [80] a codebook feature space is created by extracting dense SIFT descriptors at fixed grid locations from a training set of two-photon excitation microscopy images with different stages of liver fibrosis.…”

SkinScan^©: A portable library for melanoma detection on handheld devices

“…We have previously developed a desktop application [1] that provides end-to-end processing of skin cancer images, including preprocessing, segmentation, feature extraction, analysis, and classification, culminating in the calculation of the probability of the lesion is malignant. The system is built using Matlab (The MathWorks Inc., Natick, MA) and requires a desktop computer to run.…”