Computers are learning to distinguish whether skin lesion images are benign or malignant and require further treatment. It appears they’re on par with, if not more accurate than, many dermatologists, a study shows.
Stanford University researchers compared a computer-driven deep neural network to dermatologists’ ability to visually classify possible melanoma, basal cell or squamous cell carcinoma lesions.
“We recruited 21 board certified dermatologists and showed them 300 images where they had to classify the lesions as benign or malignant, and whether they would biopsy or reassure the patient,” says the study’s lead author Roberto A. Novoa, M.D., clinical assistant professor of dermatology and pathology, Stanford University, Stanford. “From there, the algorithm performed about as well as the dermatologists, if not better. There were dermatologists who did perform better than the algorithm but, in general, this was a proof of concept study, so … we were demonstrating the efficacy of these algorithms for making the diagnosis.”
Computer that think like humans
Deep neural networks are a type of computer algorithm within the field of artificial intelligence — a field that uses computers to mimic brain function, including reasoning.
While it might sound like a new concept, it’s not. Researchers reported on the potential for computers to diagnose facial tumors in 1986.
“The idea for neural networks has been around since the 1960s,” Dr. Novoa says. “But it was only the last five years that the computing power and technological capabilities caught up to the math.”
Today’s neural networks take in vast amounts of information. They then learn the rules that lie behind the data to derive patterns and, eventually, correct answers, according to Dr. Novoa.
“Essentially, deep neural networks are mathematical equations that are stacked in layers. They start at the most basic level by learning the edges of all the objects in an image. Then, they move on to telling you this is a triangle or a square. The next layer might indicate whether an image is a cat or a dog. Finally, there’s a layer that says this is a Belgium malinois or German shepherd,” Dr. Novoa says. “… if it gets the answer wrong, it goes back through the equation and changes the values and weights of that equation, until it gets the most answers correct for the most number of images. By doing so, it learns, over time, what’s important and what’s not.”