Melanoma is a type of malignant tumor responsible for more than 70 percent of all skin cancer-related deaths worldwide. For years, physicians have relied on visual inspection to identify suspicious pigmented lesions (SPLs), which can be an indication of skin cancer. Such early-stage identification of SPLs in primary care settings can improve melanoma prognosis and significantly reduce treatment cost.
The challenge is that quickly finding and prioritizing SPLs is difficult, due to the high volume of pigmented lesions that often need to be evaluated for potential biopsies. Now, researchers from MIT and elsewhere have devised a new artificial intelligence pipeline, using deep convolutional neural networks (DCNNs) and applying them to analyzing SPLs through the use of wide-field photography common in most smartphones and personal cameras.
How it works: A wide-field image, acquired with a smartphone camera, shows large skin sections from a patient in a primary-care setting. An automated system detects, extracts, and analyzes all pigmented skin lesions observable in the wide-field image. A pre-trained deep convolutional neural network (DCNN) determines the suspiciousness of individual pigmented lesions and marks them (yellow = consider further inspection, red = requires further inspection or referral to dermatologist). Extracted features are used to further assess pigmented lesions and to display results in a heatmap format. Animation courtesy of the researchers.
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DCNNs are neural networks that can be used to classify (or “name”) images to then cluster them (such as when performing a photo search). These machine learning algorithms belong to the subset of deep learning.
Using cameras to take wide-field photographs of large areas of patients’ bodies, the program uses DCNNs to quickly and effectively identify and screen for early-stage melanoma, according to Luis R. Soenksen, a postdoc and a medical device expert
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