Neural network model can predict melanoma.

• Position: Skin Cancer

It is a mark on your skin you never noticed before, or a spot with irregular borders, or a mole that seems to be changing. Maybe it is a wound that does not heal. Is it melanoma? These days, the only way to be sure it is not the relatively rare--but potentially lethal--skin cancer is to get a biopsy and consult a pathologist.

However, the poke and punch of traditional melanoma biopsies could, in the near future, be avoided. University of California, 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 the College of Creative Studies, so far has proven a 96% accuracy rating with his neural network model.

"We're applying computer vision to solving medical problems," says Bhattacharya, who, along with Dexter Hadley, assistant professor in the Department of Pediatrics, Pathology & Laboratory Medicine at the University of California, San Francisco, 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.

Several factors make melanoma --the rarest form of skin cancer-- difficult to detect. While skin cancer is the most common of all cancers, according to the American Cancer Society, Atlanta, Ga., melanoma accounts for only about one percent of skin cancers. Also, the disease can develop as a new mole, but it often forms over existing moles and, in some cases, can be mistaken for scars and benign growths.

Additionally, other factors such as time constraints, priority of other illnesses, and patient embarrassment frequently result in patients and doctors putting melanoma screening on the backburner. The result could be a dangerous oversight: left unchecked, the tumor can spread to other parts of the body, including bones...