Furthermore, a convolutional neural network (CNN) was trained using the generated images to assess the latter’s effect on skin lesion classification accuracy. The generated images were evaluated quantitively and qualitatively. Two deep learning methods, style transfer (ST) and deep blending (DB), were utilized to generate images with darker skin colors using the lighter skin images. Methods: We collected skin clinical images for common malignant and benign skin conditions from DermNet NZ, the International Skin Imaging Collaboration, and Dermatology Atlas. Objective: The aim of this study is to develop a deep learning approach that generates realistic images of darker skin colors to improve dermatology data diversity for various malignant and benign lesions. Artificial intelligence applications have further disadvantaged people of color because those applications are mainly trained with light skin color images. School of Computational Science and EngineeringĮmail: The lack of dark skin images in pathologic skin lesions in dermatology resources hinders the accurate diagnosis of skin lesions in people of color. JMIR Bioinformatics and Biotechnology 23 articles.JMIR Biomedical Engineering 61 articles.JMIR Perioperative Medicine 69 articles. Journal of Participatory Medicine 71 articles.JMIR Rehabilitation and Assistive Technologies 177 articles.JMIR Pediatrics and Parenting 230 articles.Interactive Journal of Medical Research 259 articles.JMIR Public Health and Surveillance 964 articles.Journal of Medical Internet Research 6898 articles.(Brightness being a different perception than lightness/darkness). The other color appearance models I mentioned above do have a saturation correlate, as well as brightness in addition to lightness. Saturation is not available with LAB, only Chroma. To determine the color difference, it is simply the euclidian distance between two colors, in other words, the square root of the sum of the squared differences, so: ∆ = ((L * 1 - L * 2) 2 + (a 1 - a 2) 2 + (b 1 - b 2) 2 ) 0.5ĬIELAB is also available with polar coordinates, LCh, for Lightness, Chroma, and hue. The channels are perceptual lightness, L * as a value from 0 to 100, and a * and b * which encode red/green and blue/yellow respectively, and are each nominally -128 to 127 if using signed 8bit integers. L *a *b * breaks the colors down based on human perception and the opponent process of vision. CIELABĪ simpler model is CIELAB which is part of OpenCV, and is a better choice than HSV or HSL, particularly if you are goal is to judge or select colors in a manner similar to human perception. These might not be available in a library for OpenCV, but most aren't that difficult to implement. Perceptual Appearance ModelsĬIECAM02, CIECAM16, J za zb z are pretty much state of the art, and there is ZCAM for HDR imagery, and also image appearance models such as iCAM. As color is not "real", and only a perception, it would follow that using a perceptually accurate appearance model is a best practice for your application. I'd suggest if you want to improve accuracy, perhaps try a perceptually accurate colorspace - and HSV isn't one.
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