Automated melanoma detection

Research from engineers at MIT “describes the development of the tool using a branch of artificial intelligence called deep convolutional neural networks (DCNNs). The researchers trained their tool using over 20,000 images, taken from 133 patients and from publicly available databases. Importantly, the pictures were taken using different personal cameras, to ensure that it would work with real-life examples. Once the tool was trained using known examples, it demonstrated over 90.3% sensitivity and 89.9% specificity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds.”  Shape, color and size of lesions were the determinants for diagnosis.  MORE WITH VIDEO

 Image Credit: MIT and MedGadget.com