These days, the only way to be sure it’s not the relatively rare but potentially lethal skin cancer is to get a biopsy and consult a pathologist.

UC Santa Barbara undergrad Abhishek Bhattacharya is using the power of artificial intelligence to help people ascertain whether that new and strange mark is, in fact, the deadly skin cancer. Bhattacharya, a biology and computer science student in UCSB’s College of Creative Studies (CCS), has so far proven a 96 percent accuracy rating with his neural network model.

“We’re applying computer vision to solving medical problems,” said Bhattacharya, who, along with UC San Francisco (UCSF) physician and professor Dexter Hadley, developed the melanoma project and trained the neural network model to judge images of skin irregularities and predict whether the mark, mole or lesion is cause for concern.

“What we’re trying to do is something humans can’t do,” said Dr. Hadley,” and humans can’t predict under–the–skin pathology from looking from the top.” Visual inspection is subjective, he said, even by physicians using a dermatoscope to see superficial and subsuperficial skin features and trained in the ABCDE (Area, Border, Color, Diameter, Evolution) protocol.

“You want to be oversensitive about this,” said Dr. Hadley, who specializes in precision medicine. For roughly every 30 excisions of suspicious moles and lesions, he estimated, one proves to be a melanoma.

And now machine learning can offer a better way. Taking advantage of major gains in computing power and big data – and inspired by how human brains work – scientists have figured out how neural network models, such as those that exist at the UCSF Institute for Computational Health Sciences, can be trained to predict whether a mole or lesion is melanoma based on images gathered from the world wide web.

“It’s an iterative process, essentially,” said Bhattacharya. Using loads of images scraped from the web, with labels indicating the type of skin lesions in those images, the neural network model learns what visual aspects are closely associated with melanoma diagnoses. This project uses a “convolutional neural network,” the architecture of which is modeled on an animal’s visual cortex.

“Basically an image is just an array of values, which tells it to be red or blue or green, for instance, and together you get an image,” Bhattacharya said. “And when a computer sees an image, it’s seeing a matrix of numbers.” As the model processes the image, different neurons on different layers are activated by various aspects of the image, in the same way that signals travel from neuron to neuron in our brains. Some lower–level neurons might be activated by edges and blobs of color, Bhattacharya said, but higher–level neurons could be more concerned with fuller concepts and classifications.

Like a human brain, the more practice the neural network model gets, the better it is at determining what the image might indicate. Fortunately, despite the rarity of melanomas — and hence relatively few images of them — the researchers were able to utilize “transfer learning.” With this method, the model was “pre–trained” in making fundamental recognitions and distinctions between images of everyday objects derived from a large repository called ImageNet, and then given medical images to inspect.

Three or so years ago, the researchers achieved roughly 85 percent accuracy with just a few hundred images. Since then, with more images and more refinement, the degree of accuracy has climbed to 96 percent — better than that of humans.

Bhattacharya, Hadley and other physicians have teamed up to launch a company called SkinIQ, aimed at bringing artificial intelligence to skin cancer detection. The team hopes to create a mobile app that will allow people to make their own assessments as to whether they may or may not have melanoma.